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A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automatically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these documents are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Comparing with the ROUGE 1.5.5 evaluation scores over TAC 2008 (formerly DUC, conducted by NIST) dataset, the proposed evaluation technique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.","publication_date":"2013,,","publication_name":"Research in Computing Science","grobid_abstract_attachment_id":"50471026"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Entailment-based Fully Automatic Technique for Evaluation of Summaries","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [35873]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":50471026,"attachmentType":"pdf"}"><img alt="First page of “Entailment-based Fully Automatic Technique for Evaluation of Summaries”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/50471026/mini_magick20190129-31069-17vmnw4.png?1548756482" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Entailment-based Fully Automatic Technique for Evaluation of Summaries</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="35873" href="https://ipn.academia.edu/AlexanderGelbukh"><img alt="Profile image of Alexander Gelbukh" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/35873/11862/15727678/s65_alexander.gelbukh.png" />Alexander Gelbukh</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2013, Research in Computing Science</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">13 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 30013429; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">We propose a fully automatic technique for evaluating text summaries without the need to prepare the gold standard summaries manually. A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automatically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these documents are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Comparing with the ROUGE 1.5.5 evaluation scores over TAC 2008 (formerly DUC, conducted by NIST) dataset, the proposed evaluation technique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":50471026,"attachmentType":"pdf","workUrl":"https://www.academia.edu/30013429/Entailment_based_Fully_Automatic_Technique_for_Evaluation_of_Summaries"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":50471026,"attachmentType":"pdf","workUrl":"https://www.academia.edu/30013429/Entailment_based_Fully_Automatic_Technique_for_Evaluation_of_Summaries"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Ferrández</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2008</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents how text summarization can be influenced by textual entailment. We show that if we use textual entailment recognition together with text summarization approach, we achieve good results for final summaries, obtaining an improvement of 6.78% with respect to the summarization approach only. We also compare the performance of this combined approach to two baselines (the one provided in DUC 2002 and ours based on word-frequency technique) and we discuss the preliminary results obtained in order to infer conclusions that can be useful for future research.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A text summarization approach under the influence of textual entailment","attachmentId":83818203,"attachmentType":"pdf","work_url":"https://www.academia.edu/73999486/A_text_summarization_approach_under_the_influence_of_textual_entailment","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/73999486/A_text_summarization_approach_under_the_influence_of_textual_entailment"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="78034536" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/78034536/ASHuR_Evaluation_of_the_Relation_Summary_Content_Without_Human_Reference_Using_ROUGE">ASHuR: Evaluation of the Relation Summary-Content Without Human Reference Using ROUGE</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="219320498" href="https://independent.academia.edu/AlandavidRamirezNoriega">Alan david Ramirez Noriega</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computing and Informatics, 2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"ASHuR: Evaluation of the Relation Summary-Content Without Human Reference Using ROUGE","attachmentId":85221079,"attachmentType":"pdf","work_url":"https://www.academia.edu/78034536/ASHuR_Evaluation_of_the_Relation_Summary_Content_Without_Human_Reference_Using_ROUGE","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/78034536/ASHuR_Evaluation_of_the_Relation_Summary_Content_Without_Human_Reference_Using_ROUGE"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="7197749" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/7197749/An_Effective_Sentence_Ordering_Approach_For_Multi_Document_Summarization_Using_Text_Entailment">An Effective Sentence Ordering Approach For Multi-Document Summarization Using Text Entailment</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4270915" href="https://ijritcc.academia.edu/ijritcc">International Journal IJRITCC</a></div><p class="ds-related-work--abstract ds2-5-body-sm">With the rapid development of modern technology electronically available textual information has increased to a considerable amount. Summarization of textual information manually from unstructured text sources creates overhead to the user, therefore a systematic approach is required. Summarization is an approach that focuses on providing the user with a condensed version of the original text but in real time applications extended document summarization is required for summarizing the text from multiple documents. The main focus of multidocument summarization is sentence ordering and ranking that arranges the collected sentences from multiple document in order to generate a well-organized summary. The improper order of extracted sentences significantly degrades readability and understandability of the summary. The existing system does multi document summarization by combining several preference measures such as chronology, probabilistic, precedence, succession, topical closeness experts to calculate the preference value between sentences. These approach to sentence ordering and ranking does not address context based similarity measure between sentences which is very essential for effective summarization. The proposed system addresses this issues through textual entailment expert system. This approach builds an entailment model which incorporates the cause and effect between sentences in the documents using the symmetric measure such as cosine similarity and non-symmetric measures such as unigram match, bigram match, longest common sub-sequence, skip gram match, stemming. The proposed system is efficient in providing user with a contextual summary which significantly improves the readability and understandability of the final coherent summary.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Effective Sentence Ordering Approach For Multi-Document Summarization Using Text Entailment","attachmentId":33824119,"attachmentType":"pdf","work_url":"https://www.academia.edu/7197749/An_Effective_Sentence_Ordering_Approach_For_Multi_Document_Summarization_Using_Text_Entailment","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/7197749/An_Effective_Sentence_Ordering_Approach_For_Multi_Document_Summarization_Using_Text_Entailment"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="4429699" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4429699/Evaluating_Summaries_Automatically_A_system_Proposal">Evaluating Summaries Automatically - A system Proposal</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="5442475" href="https://univille-br.academia.edu/AlexandreCidral">Alexandre Cidral</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2008</p><p class="ds-related-work--abstract ds2-5-body-sm">We propose in this paper an automatic evaluation procedure based on a metric which could provide summary evaluation without human assistance. Our system includes two metrics, which are presented and discussed. The first metric is based on a known and powerful statistical test, the χ 2 goodness-of-fit test, and has been used in several applications. The second metric is derived from three common metrics used to evaluate Natural Language Processing (NLP) systems, namely precision, recall and f-measure. The combination of these two metrics is intended to allow one to assess the quality of summaries quickly, cheaply and without the need of human intervention, minimizing though, the role of subjective judgment and bias.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Evaluating Summaries Automatically - A system Proposal","attachmentId":31849115,"attachmentType":"pdf","work_url":"https://www.academia.edu/4429699/Evaluating_Summaries_Automatically_A_system_Proposal","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4429699/Evaluating_Summaries_Automatically_A_system_Proposal"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="20902005" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/20902005/Text_Entailment_for_Logical_Segmentation_and_Summarization">Text Entailment for Logical Segmentation and Summarization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33463254" href="https://ubbcluj.academia.edu/DoinaTatar">Doina Tatar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">Summarization is the process of condensing a source text into a shorter version preserving its information content ([2]). This paper presents some original methods for text summarization by extraction of a single source document based on a particular intuition which is not explored till now: the logical structure of a text. The summarization relies on an original linear segmentation algorithm which we denote logical segmentation (LTT) because the score of a sentence is the number of sentences of the text which are entailed by it.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Text Entailment for Logical Segmentation and Summarization","attachmentId":41620980,"attachmentType":"pdf","work_url":"https://www.academia.edu/20902005/Text_Entailment_for_Logical_Segmentation_and_Summarization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/20902005/Text_Entailment_for_Logical_Segmentation_and_Summarization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="96245415" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/96245415/A_comprehensive_summary_informativeness_evaluation_for_RST_based_summarization_methods">A comprehensive summary informativeness evaluation for RST-based summarization methods</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="244573355" href="https://independent.academia.edu/ThiagoAlexandre82">Thiago Alexandre</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009</p><p class="ds-related-work--abstract ds2-5-body-sm">Motivated by governmental, commercial and academic interests, automatic text summarization area has experienced an increasing number of researches and products, which led to a countless number of summarization methods. In this paper, we present a comprehensive comparative evaluation of the main automatic text summarization methods based on Rhetorical Structure Theory (RST), claimed to be among the best ones. Additionally, we test machine learning techniques trained on RST features. We also compare our results to superficial summarizers, which belong to a paradigm with severe limitations, and to hybrid methods, combining RST and superficial methods. Our results show that all RST methods have similar overall performance and that they outperform the superficial methods. In terms of precision, the method we propose is the best one, while it competes with other ones for coverage. Machine learning techniques achieved high accuracy in the classification of text segments worth of being in t...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A comprehensive summary informativeness evaluation for RST-based summarization methods","attachmentId":98197745,"attachmentType":"pdf","work_url":"https://www.academia.edu/96245415/A_comprehensive_summary_informativeness_evaluation_for_RST_based_summarization_methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/96245415/A_comprehensive_summary_informativeness_evaluation_for_RST_based_summarization_methods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="20901992" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/20901992/Summarization_by_logic_segmentation_and_text_entailment">Summarization by logic segmentation and text entailment</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="67743642" href="https://kindai.academia.edu/EmmaTamaianuMorita">Emma Tamaianu-Morita</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33463254" href="https://ubbcluj.academia.edu/DoinaTatar">Doina Tatar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Advances in Natural …, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">As the phenomenon of information overload grows day by day, systems that can automatically summarize documents become increasingly studied and used. This paper presents some original methods for text summarization of a single source document by extraction. The methods are based on some of our own text segmentation algorithms. We denote them logical segmentations because for all these methods the score of a sentence is the number of sentences of the text which are entailed by it. The first method of segmentation and summarization is called Logical TextTiling (LTT): for a sequence of sentences, the scores form a structure which indicates how the most important sentences alternate with ones less important and organizes the text according to its logical content. The second method, Pure Entailment uses definition of the relation of entailment between two texts. The third original method applies Dynamic Programming and centering theory to the sentences logically scored as above. The obtained ranked logical segments are used in the summarization. Our methods of segmentation and summarization are applied and evaluated against a manually realized segmentation and summarization of the same text by Donald Richie, "The Koan", . The text is reproduced at [14].</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Summarization by logic segmentation and text entailment","attachmentId":41620968,"attachmentType":"pdf","work_url":"https://www.academia.edu/20901992/Summarization_by_logic_segmentation_and_text_entailment","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/20901992/Summarization_by_logic_segmentation_and_text_entailment"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="64503942" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/64503942/Evaluation_Measures_for_Text_Summarization">Evaluation Measures for Text Summarization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4123968" href="https://zcu.academia.edu/KarelJe%C5%BEek">Karel Ježek</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computing and Informatics / Computers and Artificial Intelligence, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">We explain the ideas of automatic text summarization approaches and the taxonomy of summary evaluation methods. Moreover, we propose a new evaluation measure for assessing the quality of a summary. The core of the measure is covered by Latent Semantic Analysis (LSA) which can capture the main topics of a document. The summarization systems are ranked according to the similarity of the main topics of their summaries and their reference documents. Results show a high correlation between human rankings and the LSA-based evaluation measure. The measure is designed to compare a summary with its full text. It can compare a summary with a human written abstract as well; however, in this case using a standard ROUGE measure gives more precise results. Nevertheless, if abstracts are not available for a given corpus, using the LSA-based measure is an appropriate choice.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Evaluation Measures for Text Summarization","attachmentId":76509928,"attachmentType":"pdf","work_url":"https://www.academia.edu/64503942/Evaluation_Measures_for_Text_Summarization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/64503942/Evaluation_Measures_for_Text_Summarization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="92427291" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/92427291/Evaluation_of_automatic_summaries">Evaluation of automatic summaries</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="249116283" href="https://independent.academia.edu/HoaDang122">Hoa Dang</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 2009 Workshop on Language Generation and Summarisation - UCNLG+Sum '09, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">In evaluation of automatic summaries, it is necessary to employ multiple topics and human-produced models in order for the assessment to be stable and reliable. However, providing multiple topics and models is costly and time-consuming. This paper examines the relation between the number of available models and topics and the correlations with human judgment obtained by automatic metrics ROUGE and BE, as well as the manual Pyramid method. Testing all these methods on the same data set, taken from the TAC 2008 Summarization track, allows us to compare and contrast the methods under different conditions.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Evaluation of automatic summaries","attachmentId":95438778,"attachmentType":"pdf","work_url":"https://www.academia.edu/92427291/Evaluation_of_automatic_summaries","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/92427291/Evaluation_of_automatic_summaries"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="25428735" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/25428735/SUMMAC_a_text_summarization_evaluation">SUMMAC: a text summarization evaluation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="48815623" href="https://independent.academia.edu/LynetteHirschman">Lynette Hirschman</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Natural Language Engineering, 2002</p><p class="ds-related-work--abstract ds2-5-body-sm">The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons between text passages. However, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems were indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as well as evaluation of other 'output-related' NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"SUMMAC: a text summarization evaluation","attachmentId":45745505,"attachmentType":"pdf","work_url":"https://www.academia.edu/25428735/SUMMAC_a_text_summarization_evaluation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/25428735/SUMMAC_a_text_summarization_evaluation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":50471026,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":50471026,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_50471026" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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