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text summarization</title> <meta name="description" content="Search results for: extractive text summarization"> <meta name="keywords" content="extractive text summarization"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="extractive text summarization"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1375</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: extractive text summarization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1375</span> Programmed Speech to Text Summarization Using Graph-Based Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamsini%20Pulugurtha">Hamsini Pulugurtha</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20S.%20L.%20Jagadamba"> P. V. S. L. Jagadamba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siamese%20neural%20network" title="Siamese neural network">Siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20speech" title=" English speech"> English speech</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20text" title=" English text"> English text</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20extractive%20text%20summarization" title=" unsupervised extractive text summarization"> unsupervised extractive text summarization</a> </p> <a href="https://publications.waset.org/abstracts/143079/programmed-speech-to-text-summarization-using-graph-based-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1374</span> Graph-Based Semantical Extractive Text Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Samizadeh">Mina Samizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction" title="keyword extraction">keyword extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=n-gram%20extraction" title=" n-gram extraction"> n-gram extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20clustering" title=" topic clustering"> topic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title=" semantic analysis"> semantic analysis</a> </p> <a href="https://publications.waset.org/abstracts/160526/graph-based-semantical-extractive-text-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1373</span> Optimized Text Summarization Model on Mobile Screens for Sight-Interpreters: An Empirical Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhua%20Wang">Jianhua Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To obtain key information quickly from long texts on small screens of mobile devices, sight-interpreters need to establish optimized summarization model for fast information retrieval. Four summarization models based on previous studies were studied including title+key words (TKW), title+topic sentences (TTS), key words+topic sentences (KWTS) and title+key words+topic sentences (TKWTS). Psychological experiments were conducted on the four models for three different genres of interpreting texts to establish the optimized summarization model for sight-interpreters. This empirical study shows that the optimized summarization model for sight-interpreters to quickly grasp the key information of the texts they interpret is title+key words (TKW) for cultural texts, title+key words+topic sentences (TKWTS) for economic texts and topic sentences+key words (TSKW) for political texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=different%20genres" title="different genres">different genres</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20screens" title=" mobile screens"> mobile screens</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20summarization%20models" title=" optimized summarization models"> optimized summarization models</a>, <a href="https://publications.waset.org/abstracts/search?q=sight-interpreters" title=" sight-interpreters"> sight-interpreters</a> </p> <a href="https://publications.waset.org/abstracts/57345/optimized-text-summarization-model-on-mobile-screens-for-sight-interpreters-an-empirical-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57345.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1372</span> Video Summarization: Techniques and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaynab%20El%20Khattabi">Zaynab El Khattabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Youness%20Tabii"> Youness Tabii</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Benkaddour"> Abdelhamid Benkaddour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20summarization" title=" static summarization"> static summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20skimming" title=" video skimming"> video skimming</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20features" title=" semantic features"> semantic features</a> </p> <a href="https://publications.waset.org/abstracts/27644/video-summarization-techniques-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1371</span> Surveillance Video Summarization Based on Histogram Differencing and Sum Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Jasim%20Habeeb">Nada Jasim Habeeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Saad%20Mohammed"> Rana Saad Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Muntaha%20Khudair%20Abbass"> Muntaha Khudair Abbass </a> </p> <p class="card-text"><strong>Abstract:</strong></p> For more efficient and fast video summarization, this paper presents a surveillance video summarization method. The presented method works to improve video summarization technique. This method depends on temporal differencing to extract most important data from large video stream. This method uses histogram differencing and Sum Conditional Variance which is robust against to illumination variations in order to extract motion objects. The experimental results showed that the presented method gives better output compared with temporal differencing based summarization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temporal%20differencing" title="temporal differencing">temporal differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title=" video summarization"> video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20differencing" title=" histogram differencing"> histogram differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20conditional%20variance" title=" sum conditional variance"> sum conditional variance</a> </p> <a href="https://publications.waset.org/abstracts/54404/surveillance-video-summarization-based-on-histogram-differencing-and-sum-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1370</span> EduEasy: Smart Learning Assistant System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Karunasena">A. Karunasena</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Bandara"> P. Bandara</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20A.%20T.%20P.%20Jayasuriya"> J. A. T. P. Jayasuriya</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20D.%20Gallage"> P. D. Gallage</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20M.%20S.%20D.%20Jayasundara"> J. M. S. D. Jayasundara</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20P.%20Y.%20P.%20Nuwanjaya"> L. A. P. Y. P. Nuwanjaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Usage of smart learning concepts has increased rapidly all over the world recently as better teaching and learning methods. Most educational institutes such as universities are experimenting those concepts with their students. Smart learning concepts are especially useful for students to learn better in large classes. In large classes, the lecture method is the most popular method of teaching. In the lecture method, the lecturer presents the content mostly using lecture slides, and the students make their own notes based on the content presented. However, some students may find difficulties with the above method due to various issues such as speed in delivery. The purpose of this research is to assist students in large classes in the following content. The research proposes a solution with four components, namely note-taker, slide matcher, reference finder, and question presenter, which are helpful for the students to obtain a summarized version of the lecture note, easily navigate to the content and find resources, and revise content using questions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20summarization" title="automatic summarization">automatic summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=extractive%20text%20summarization" title=" extractive text summarization"> extractive text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition%20library" title=" speech recognition library"> speech recognition library</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20extraction" title=" sentence extraction"> sentence extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20web%20search" title=" automatic web search"> automatic web search</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20question%20generator" title=" automatic question generator"> automatic question generator</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20scoring" title=" sentence scoring"> sentence scoring</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20term%20weight" title=" the term weight"> the term weight</a> </p> <a href="https://publications.waset.org/abstracts/131469/edueasy-smart-learning-assistant-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1369</span> Feature-Based Summarizing and Ranking from Customer Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dim%20En%20Nyaung">Dim En Nyaung</a>, <a href="https://publications.waset.org/abstracts/search?q=Thin%20Lai%20Lai%20Thein"> Thin Lai Lai Thein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title="opinion mining">opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20summarization" title=" opinion summarization"> opinion summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/25595/feature-based-summarizing-and-ranking-from-customer-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1368</span> Linguistic Summarization of Structured Patent Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Y.%20Igde">E. Y. Igde</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Aydogan"> S. Aydogan</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20E.%20Boran"> F. E. Boran</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Akay"> D. Akay </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patent data have an increasingly important role in economic growth, innovation, technical advantages and business strategies and even in countries competitions. Analyzing of patent data is crucial since patents cover large part of all technological information of the world. In this paper, we have used the linguistic summarization technique to prove the validity of the hypotheses related to patent data stated in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20summarization" title=" linguistic summarization"> linguistic summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20data" title=" patent data"> patent data</a> </p> <a href="https://publications.waset.org/abstracts/74491/linguistic-summarization-of-structured-patent-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1367</span> Olefin and Paraffin Separation Using Simulations on Extractive Distillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Naeem">Muhammad Naeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20A.%20Al-Rabiah"> Abdulrahman A. Al-Rabiah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technical mixture of C4 containing 1-butene and n-butane are very close to each other with respect to their boiling points i.e. -6.3°C for 1-butene and -1°C for n-butane. Extractive distillation process is used for the separation of 1-butene from the existing mixture of C4. The solvent is the essential of extractive distillation, and an appropriate solvent shows an important role in the process economy of extractive distillation. Aspen Plus has been applied for the separation of these hydrocarbons as a simulator; moreover NRTL activity coefficient model was used in the simulation. This model indicated that the material balances in this separation process were accurate for several solvent flow rates. Mixture of acetonitrile and water used as a solvent and 99 % pure 1-butene was separated. This simulation proposed the ratio of the feed to solvent as 1 : 7.9 and 15 plates for the solvent recovery column, previously feed to solvent ratio was more than this and the proposed plates were 30, which can economize the separation process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extractive%20distillation" title="extractive distillation">extractive distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=1-butene" title=" 1-butene"> 1-butene</a>, <a href="https://publications.waset.org/abstracts/search?q=Aspen%20Plus" title=" Aspen Plus"> Aspen Plus</a>, <a href="https://publications.waset.org/abstracts/search?q=ACN%20solvent" title=" ACN solvent "> ACN solvent </a> </p> <a href="https://publications.waset.org/abstracts/10500/olefin-and-paraffin-separation-using-simulations-on-extractive-distillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10500.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1366</span> Process Simulation of 1-Butene Separation from C4 Mixture by Extractive Distillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Naeem">Muhammad Naeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20A.%20Al-Rabiah"> Abdulrahman A. Al-Rabiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Wasif%20Mughees"> Wasif Mughees</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technical mixture of C4 containing 1-butene and n-butane are very close to each other with regard to their boiling points i.e. -6.3°C for 1-butene and -1°C for n-butane. Extractive distillation process is used for the separation of 1-butene from the existing mixture of C4. The solvent is the essential of extractive distillation, and an appropriate solvent plays an important role in the process economy of extractive distillation. Aspen Plus has been applied for the separation of these hydrocarbons as a simulator. Moreover, NRTL activity coefficient model was used in the simulation. This model indicated that the material balances in this separation process were accurate for several solvent flow rates. Mixture of acetonitrile and water used as a solvent and 99% pure 1-butene was separated. This simulation proposed the ratio of the feed to solvent as 1: 7.9 and 15 plates for the solvent recovery column. Previously feed to solvent ratio was more than this and the number of proposed plates were 30, which shows that the separation process can be economized. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extractive%20distillation" title="extractive distillation">extractive distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=1-butene" title=" 1-butene"> 1-butene</a>, <a href="https://publications.waset.org/abstracts/search?q=aspen%20plus" title=" aspen plus"> aspen plus</a>, <a href="https://publications.waset.org/abstracts/search?q=ACN%20solvent" title=" ACN solvent"> ACN solvent</a> </p> <a href="https://publications.waset.org/abstracts/5813/process-simulation-of-1-butene-separation-from-c4-mixture-by-extractive-distillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">544</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1365</span> Recovery of Acetonitrile from Aqueous Solutions by Extractive Distillation: The Effect of Entrainer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Y.%20Sazonova">Aleksandra Y. Sazonova</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentina%20M.%20Raeva"> Valentina M. Raeva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this work was to apply extractive distillation for acetonitrile removal from water solutions, to validate thermodynamic criterion based on excess Gibbs energy to entrainer selection process for acetonitrile – water mixture separation and show its potential efficiency at isothermal conditions as well as at isobaric (conditions of real distillation process), to simulate and analyze an extractive distillation process with chosen entrainers: optimize amount of trays and feeds, entrainer/original mixture and reflux ratios. Equimolar composition of the feed stream was chosen for the process, comparison of the energy consumptions was carried out. Glycerol was suggested as the most energetically and ecologically suitable entrainer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acetonitrile" title="acetonitrile">acetonitrile</a>, <a href="https://publications.waset.org/abstracts/search?q=entrainer" title=" entrainer"> entrainer</a>, <a href="https://publications.waset.org/abstracts/search?q=extractive%20distillation" title=" extractive distillation"> extractive distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=water" title=" water"> water</a> </p> <a href="https://publications.waset.org/abstracts/15373/recovery-of-acetonitrile-from-aqueous-solutions-by-extractive-distillation-the-effect-of-entrainer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15373.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1364</span> Simulation Data Summarization Based on Spatial Histograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhao">Jing Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshiharu%20Ishikawa"> Yoshiharu Ishikawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuan%20Xiao"> Chuan Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kento%20Sugiura"> Kento Sugiura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simulation%20data" title="simulation data">simulation data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20summarization" title=" data summarization"> data summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20histograms" title=" spatial histograms"> spatial histograms</a>, <a href="https://publications.waset.org/abstracts/search?q=exploration" title=" exploration"> exploration</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/98571/simulation-data-summarization-based-on-spatial-histograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98571.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1363</span> Extractive Fermentation of Ethanol Using Vacuum Fractionation Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weeraya%20Samnuknit">Weeraya Samnuknit</a>, <a href="https://publications.waset.org/abstracts/search?q=Apichat%20Boontawan"> Apichat Boontawan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A vacuum fractionation technique was introduced to remove ethanol from fermentation broth. The effect of initial glucose and ethanol concentrations were investigated for specific productivity. The inhibitory ethanol concentration was observed at 100 g/L. In order to increase the fermentation performance, the ethanol product was removed as soon as it is produced. The broth was boiled at 35°C by reducing the pressure to 65 mBar. The ethanol/water vapor was fractionated for up to 90 wt% before leaving the column. Ethanol concentration in the broth was kept lower than 25 g/L, thus minimized the product inhibition effect to the yeast cells. For batch extractive fermentation, a high substrate utilization rate was obtained at 26.6 g/L.h and most of glucose was consumed within 21 h. For repeated-batch extractive fermentation, addition of glucose was carried out up to 9 times and ethanol was produced more than 8-fold higher than batch fermentation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ethanol" title="ethanol">ethanol</a>, <a href="https://publications.waset.org/abstracts/search?q=extractive%20fermentation" title=" extractive fermentation"> extractive fermentation</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20inhibition" title=" product inhibition"> product inhibition</a>, <a href="https://publications.waset.org/abstracts/search?q=vacuum%20fractionation" title=" vacuum fractionation"> vacuum fractionation</a> </p> <a href="https://publications.waset.org/abstracts/12965/extractive-fermentation-of-ethanol-using-vacuum-fractionation-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12965.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1362</span> Deep Learning-Based Approach to Automatic Abstractive Summarization of Patent Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sakshi%20V.%20Tantak">Sakshi V. Tantak</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishap%20K.%20Malik"> Vishap K. Malik</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelanjney%20Pilarisetty"> Neelanjney Pilarisetty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abstractive%20summarization" title="abstractive summarization">abstractive summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20Processing" title=" natural language Processing"> natural language Processing</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20document" title=" patent document"> patent document</a> </p> <a href="https://publications.waset.org/abstracts/135403/deep-learning-based-approach-to-automatic-abstractive-summarization-of-patent-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1361</span> Key Frame Based Video Summarization via Dependency Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janya%20Sainui">Janya Sainui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a rapid growth of digital videos and data communications, video summarization that provides a shorter version of the video for fast video browsing and retrieval is necessary. Key frame extraction is one of the mechanisms to generate video summary. In general, the extracted key frames should both represent the entire video content and contain minimum redundancy. However, most of the existing approaches heuristically select key frames; hence, the selected key frames may not be the most different frames and/or not cover the entire content of a video. In this paper, we propose a method of video summarization which provides the reasonable objective functions for selecting key frames. In particular, we apply a statistical dependency measure called quadratic mutual informaion as our objective functions for maximizing the coverage of the entire video content as well as minimizing the redundancy among selected key frames. The proposed key frame extraction algorithm finds key frames as an optimization problem. Through experiments, we demonstrate the success of the proposed video summarization approach that produces video summary with better coverage of the entire video content while less redundancy among key frames comparing to the state-of-the-art approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame%20extraction" title=" key frame extraction"> key frame extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20measure" title=" dependency measure"> dependency measure</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20mutual%20information" title=" quadratic mutual information"> quadratic mutual information</a> </p> <a href="https://publications.waset.org/abstracts/75218/key-frame-based-video-summarization-via-dependency-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75218.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">266</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1360</span> An Experiential Learning of Ontology-Based Multi-document Summarization by Removal Summarization Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pranjali%20Avinash%20Yadav-Deshmukh">Pranjali Avinash Yadav-Deshmukh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disaster%20management" title="disaster management">disaster management</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction%20technique" title=" extraction technique"> extraction technique</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-document%20summarization" title=" multi-document summarization"> multi-document summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20extraction" title=" sentence extraction"> sentence extraction</a> </p> <a href="https://publications.waset.org/abstracts/32426/an-experiential-learning-of-ontology-based-multi-document-summarization-by-removal-summarization-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32426.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1359</span> Unlocking the Potential of Short Texts with Semantic Enrichment, Disambiguation Techniques, and Context Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouheb%20Mehdoui">Mouheb Mehdoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Fraisse"> Amel Fraisse</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the potential of short texts through semantic enrichment and disambiguation techniques. By employing context fusion, we aim to enhance the comprehension and utility of concise textual information. The methodologies utilized are grounded in recent advancements in natural language processing, which allow for a deeper understanding of semantics within limited text formats. Specifically, topic classification is employed to understand the context of the sentence and assess the relevance of added expressions. Additionally, word sense disambiguation is used to clarify unclear words, replacing them with more precise terms. The implications of this research extend to various applications, including information retrieval and knowledge representation. Ultimately, this work highlights the importance of refining short text processing techniques to unlock their full potential in real-world applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20traffic" title="information traffic">information traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=word-sense%20disambiguation" title=" word-sense disambiguation"> word-sense disambiguation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20enrichment" title=" semantic enrichment"> semantic enrichment</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity%20resolution" title=" ambiguity resolution"> ambiguity resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20text%20enhancement" title=" short text enhancement"> short text enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual%20understanding" title=" contextual understanding"> contextual understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity" title=" ambiguity"> ambiguity</a> </p> <a href="https://publications.waset.org/abstracts/193872/unlocking-the-potential-of-short-texts-with-semantic-enrichment-disambiguation-techniques-and-context-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">8</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1358</span> A Method for Clinical Concept Extraction from Medical Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moshe%20Wasserblat">Moshe Wasserblat</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Mamou"> Jonathan Mamou</a>, <a href="https://publications.waset.org/abstracts/search?q=Oren%20Pereg"> Oren Pereg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Processing (NLP) has made a major leap in the last few years, in practical integration into medical solutions; for example, extracting clinical concepts from medical texts such as medical condition, medication, treatment, and symptoms. However, training and deploying those models in real environments still demands a large amount of annotated data and NLP/Machine Learning (ML) expertise, which makes this process costly and time-consuming. We present a practical and efficient method for clinical concept extraction that does not require costly labeled data nor ML expertise. The method includes three steps: Step 1- the user injects a large in-domain text corpus (e.g., PubMed). Then, the system builds a contextual model containing vector representations of concepts in the corpus, in an unsupervised manner (e.g., Phrase2Vec). Step 2- the user provides a seed set of terms representing a specific medical concept (e.g., for the concept of the symptoms, the user may provide: ‘dry mouth,’ ‘itchy skin,’ and ‘blurred vision’). Then, the system matches the seed set against the contextual model and extracts the most semantically similar terms (e.g., additional symptoms). The result is a complete set of terms related to the medical concept. Step 3 –in production, there is a need to extract medical concepts from the unseen medical text. The system extracts key-phrases from the new text, then matches them against the complete set of terms from step 2, and the most semantically similar will be annotated with the same medical concept category. As an example, the seed symptom concepts would result in the following annotation: “The patient complaints on fatigue [symptom], dry skin [symptom], and Weight loss [symptom], which can be an early sign for Diabetes.” Our evaluations show promising results for extracting concepts from medical corpora. The method allows medical analysts to easily and efficiently build taxonomies (in step 2) representing their domain-specific concepts, and automatically annotate a large number of texts (in step 3) for classification/summarization of medical reports. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20concepts" title="clinical concepts">clinical concepts</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20expansion" title=" concept expansion"> concept expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20records%20annotation" title=" medical records annotation"> medical records annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20records%20summarization" title=" medical records summarization "> medical records summarization </a> </p> <a href="https://publications.waset.org/abstracts/116135/a-method-for-clinical-concept-extraction-from-medical-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1357</span> Extractive Bioconversion of Polyhydroxyalkanoates (PHAs) from Ralstonia Eutropha Via Aqueous Two-Phase System-An Integrated Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20K.%20Leong">Y. K. Leong</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20C.%20W.%20Lan"> J. C. W. Lan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Loh"> H. S. Loh</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20L.%20Show"> P. L. Show</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Being biodegradable, non-toxic, renewable and have similar or better properties as commercial plastics, polyhydroxy alkanoates (PHAs) can be a potential game changer in the polymer industry. PHAs are the biodegradable polymer produced by bacteria, which are in interest as a sustainable alternative to petrochemical-derived plastics; however, its commercial value has significantly limited by high production and recovery cost of PHA. Aqueous two-phase system (ATPS) offers different chemical and physical environments, which contains about 80-90% water delivers an excellent environment for partitioning of cells, cell organelles and biologically active substances. Extractive bioconversion via ATPS allows the integration of PHA upstream fermentation and downstream purification process, which reduces production steps and time, thus lead to cost reduction. The ability of Ralstonia eutropha to grow under different ATPS conditions was investigated for its potential to be used in a bioconversion system. Changes in tie-line length (TLL) and a volume ratio (Vr) were shown to have an effect on PHA partition coefficient. High PHA recovery yield of 65% with a relatively high purity of 73% was obtained in PEG 6000/Sodium sulphate system with 42.6 wt/wt % TLL and 1.25 Vr. Extractive bioconversion via ATPS is an attractive approach for the combination of PHA production and recovery process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aqueous%20two-phase%20system" title="aqueous two-phase system">aqueous two-phase system</a>, <a href="https://publications.waset.org/abstracts/search?q=extractive%20bioconversion" title=" extractive bioconversion"> extractive bioconversion</a>, <a href="https://publications.waset.org/abstracts/search?q=polyhydroxy%20alkanoates" title=" polyhydroxy alkanoates"> polyhydroxy alkanoates</a>, <a href="https://publications.waset.org/abstracts/search?q=purification" title=" purification "> purification </a> </p> <a href="https://publications.waset.org/abstracts/40313/extractive-bioconversion-of-polyhydroxyalkanoates-phas-from-ralstonia-eutropha-via-aqueous-two-phase-system-an-integrated-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40313.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">308</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1356</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">601</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1355</span> A Summary-Based Text Classification Model for Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Liu">Shuo Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20natural%20language%20processing" title="Chinese natural language processing">Chinese natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=abstract%20extraction" title=" abstract extraction"> abstract extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a> </p> <a href="https://publications.waset.org/abstracts/158060/a-summary-based-text-classification-model-for-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158060.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1354</span> Urdu Text Extraction Method from Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samabia%20Tehsin">Samabia Tehsin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Kausar"> Sumaira Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=caption%20text" title="caption text">caption text</a>, <a href="https://publications.waset.org/abstracts/search?q=content-based%20image%20retrieval" title=" content-based image retrieval"> content-based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20extraction" title=" text extraction"> text extraction</a> </p> <a href="https://publications.waset.org/abstracts/9566/urdu-text-extraction-method-from-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1353</span> Fuzzy Inference-Assisted Saliency-Aware Convolution Neural Networks for Multi-View Summarization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tanveer%20Hussain">Tanveer Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Khan%20Muhammad"> Khan Muhammad</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Ullah"> Amin Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mi%20Young%20Lee"> Mi Young Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung%20Wook%20Baik"> Sung Wook Baik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20video%20data%20analysis" title="big video data analysis">big video data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view%20video%20summarization" title=" multi-view video summarization"> multi-view video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=saliency%20detection" title=" saliency detection"> saliency detection</a> </p> <a href="https://publications.waset.org/abstracts/135176/fuzzy-inference-assisted-saliency-aware-convolution-neural-networks-for-multi-view-summarization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135176.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1352</span> Small Text Extraction from Documents and Chart Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rominkumar%20Busa">Rominkumar Busa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahira%20K.%20C."> Shahira K. C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijiya%20A."> Lijiya A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20text%20extraction" title="small text extraction">small text extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20text%20recognition" title=" scene text recognition"> scene text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a> </p> <a href="https://publications.waset.org/abstracts/150310/small-text-extraction-from-documents-and-chart-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1351</span> Microwave Assisted Extractive Desulfurization of Gas Oil Feedstock</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamida%20Y.%20Mostafa">Hamida Y. Mostafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20E.%20Khedr"> Ghada E. Khedr</a>, <a href="https://publications.waset.org/abstracts/search?q=Dina%20M.%20Abd%20El-Aty"> Dina M. Abd El-Aty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sulfur compound removal from petroleum fractions is a critical component of environmental protection demands. Solvent extraction, oxidative desulfurization, or hydro-treatment techniques have traditionally been used as the removal processes. While all methods were capable of eliminating sulfur compounds at moderate rates, they had some limitations. A major problem with these routes is their high running expenses, which are caused by their prolonged operation times and high energy consumption. Therefore, new methods for removing sulfur are still necessary. In the current study, a simple assisted desulfurization system for gas oil fraction has been successfully developed using acetonitrile and methanol as a solvent under microwave irradiation. The key variables affecting sulfur removal have been studied, including microwave power, irradiation time, and solvent to gas oil volume ratio. At the conclusion of the research that is being presented, promising results have been found. The results show that a microwave-assisted extractive desulfurization method had remove sulfur with a high degree of efficiency under the suitable conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extractive%20desulfurization" title="extractive desulfurization">extractive desulfurization</a>, <a href="https://publications.waset.org/abstracts/search?q=microwave%20assisted%20extraction" title=" microwave assisted extraction"> microwave assisted extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=petroleum%20fractions" title=" petroleum fractions"> petroleum fractions</a>, <a href="https://publications.waset.org/abstracts/search?q=acetonitrile%20and%20methanol" title=" acetonitrile and methanol"> acetonitrile and methanol</a> </p> <a href="https://publications.waset.org/abstracts/167883/microwave-assisted-extractive-desulfurization-of-gas-oil-feedstock" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167883.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1350</span> Text Data Preprocessing Library: Bilingual Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kabil%20Boukhari">Kabil Boukhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on diﬀerent corpora and gave an interesting result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20preprocessing" title="text preprocessing">text preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization" title=" normalization"> normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20generation" title=" text generation"> text generation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/150846/text-data-preprocessing-library-bilingual-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150846.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1349</span> Synthesis, Characterization, and Application of Novel Trihexyltetradecyl Phosphonium Chloride for Extractive Desulfurization of Liquid Fuel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swapnil%20A.%20Dharaskar">Swapnil A. Dharaskar</a>, <a href="https://publications.waset.org/abstracts/search?q=Kailas%20L.%20Wasewar"> Kailas L. Wasewar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahesh%20N.%20Varma"> Mahesh N. Varma</a>, <a href="https://publications.waset.org/abstracts/search?q=Diwakar%20Z.%20Shende"> Diwakar Z. Shende</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Owing to the stringent environmental regulations in many countries for production of ultra low sulfur petroleum fractions intending to reduce sulfur emissions results in enormous interest in this area among the scientific community. The requirement of zero sulfur emissions enhances the prominence for more advanced techniques in desulfurization. Desulfurization by extraction is a promising approach having several advantages over conventional hydrodesulphurization. Present work is dealt with various new approaches for desulfurization of ultra clean gasoline, diesel and other liquid fuels by extraction with ionic liquids. In present paper experimental data on extractive desulfurization of liquid fuel using trihexyl tetradecyl phosphonium chloride has been presented. The FTIR, 1H-NMR, and 13C-NMR have been discussed for the molecular confirmation of synthesized ionic liquid. Further, conductivity, solubility, and viscosity analysis of ionic liquids were carried out. The effects of reaction time, reaction temperature, sulfur compounds, ultrasonication, and recycling of ionic liquid without regeneration on removal of dibenzothiphene from liquid fuel were also investigated. In extractive desulfurization process, the removal of dibenzothiophene in n-dodecane was 84.5% for mass ratio of 1:1 in 30 min at 30OC under the mild reaction conditions. Phosphonium ionic liquids could be reused five times without a significant decrease in activity. Also, the desulfurization of real fuels, multistage extraction was examined. The data and results provided in present paper explore the significant insights of phosphonium based ionic liquids as novel extractant for extractive desulfurization of liquid fuels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ionic%20liquid" title="ionic liquid">ionic liquid</a>, <a href="https://publications.waset.org/abstracts/search?q=PPIL" title=" PPIL"> PPIL</a>, <a href="https://publications.waset.org/abstracts/search?q=desulfurization" title=" desulfurization"> desulfurization</a>, <a href="https://publications.waset.org/abstracts/search?q=liquid%20fuel" title=" liquid fuel"> liquid fuel</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a> </p> <a href="https://publications.waset.org/abstracts/15849/synthesis-characterization-and-application-of-novel-trihexyltetradecyl-phosphonium-chloride-for-extractive-desulfurization-of-liquid-fuel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">609</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1348</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1347</span> On-Road Text Detection Platform for Driver Assistance Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guezouli%20Larbi">Guezouli Larbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Soundes"> Belkacem Soundes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title="text detection">text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=PZM" title=" PZM"> PZM</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/161507/on-road-text-detection-platform-for-driver-assistance-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161507.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">83</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1346</span> Detecting Paraphrases in Arabic Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amal%20Alshahrani">Amal Alshahrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Allan%20Ramsay"> Allan Ramsay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=TF-IDF" title=" TF-IDF"> TF-IDF</a>, <a href="https://publications.waset.org/abstracts/search?q=cosine%20similarity" title=" cosine similarity"> cosine similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20time%20warping%20%28DTW%29" title=" dynamic time warping (DTW)"> dynamic time warping (DTW)</a> </p> <a href="https://publications.waset.org/abstracts/35776/detecting-paraphrases-in-arabic-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge 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