-
Atanassova, I.; Bertin, M.; Larivière, V.: On the composition of scientific abstracts (2016)
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- Abstract
- Purpose - Scientific abstracts reproduce only part of the information and the complexity of argumentation in a scientific article. The purpose of this paper provides a first analysis of the similarity between the text of scientific abstracts and the body of articles, using sentences as the basic textual unit. It contributes to the understanding of the structure of abstracts. Design/methodology/approach - Using sentence-based similarity metrics, the authors quantify the phenomenon of text re-use in abstracts and examine the positions of the sentences that are similar to sentences in abstracts in the introduction, methods, results and discussion structure, using a corpus of over 85,000 research articles published in the seven Public Library of Science journals. Findings - The authors provide evidence that 84 percent of abstract have at least one sentence in common with the body of the paper. Studying the distributions of sentences in the body of the articles that are re-used in abstracts, the authors show that there exists a strong relation between the rhetorical structure of articles and the zones that authors re-use when writing abstracts, with sentences mainly coming from the beginning of the introduction and the end of the conclusion. Originality/value - Scientific abstracts contain what is considered by the author(s) as information that best describe documents' content. This is a first study that examines the relation between the contents of abstracts and the rhetorical structure of scientific articles. The work might provide new insight for improving automatic abstracting tools as well as information retrieval approaches, in which text organization and structure are important features.
-
Sparck Jones, K.: Automatic summarising : the state of the art (2007)
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- Abstract
- This paper reviews research on automatic summarising in the last decade. This work has grown, stimulated by technology and by evaluation programmes. The paper uses several frameworks to organise the review, for summarising itself, for the factors affecting summarising, for systems, and for evaluation. The review examines the evaluation strategies applied to summarising, the issues they raise, and the major programmes. It considers the input, purpose and output factors investigated in recent summarising research, and discusses the classes of strategy, extractive and non-extractive, that have been explored, illustrating the range of systems built. The conclusions drawn are that automatic summarisation has made valuable progress, with useful applications, better evaluation, and more task understanding. But summarising systems are still poorly motivated in relation to the factors affecting them, and evaluation needs taking much further to engage with the purposes summaries are intended to serve and the contexts in which they are used.
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Dorr, B.J.; Gaasterland, T.: Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction (2007)
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Endres-Niggemeyer, B.; Ziegert, C.: SummIt-BMT : (Summarize It in BMT) in Diagnose und Therapie, Abschlussbericht (2002)
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- Abstract
- SummIt-BMT (Summarize It in Bone Marrow Transplantation) - das Zielsystem des Projektes - soll Ärzten in der Knochenmarktransplantation durch kognitiv fundiertes Zusammenfassen (Endres-Niggemeyer, 1998) aus dem WWW eine schnelle Informationsaufnahme ermöglichen. Im bmbffinanzierten Teilprojekt, über das hier zu berichten ist, liegt der Schwerpunkt auf den klinischen Fragestellungen. SummIt-BMT hat als zentrale Komponente eine KMT-Ontologie. Den Systemablauf veranschaulicht Abb. 1: Benutzer geben ihren Informationsbedarf in ein strukturiertes Szenario ein. Sie ziehen dazu Begriffe aus der Ontologie heran. Aus dem Szenario werden Fragen an Suchmaschinen abgeleitet. Die Summit-BMT-Metasuchmaschine stößt Google an und sucht in Medline, der zentralen Literaturdatenbank der Medizin. Das Suchergebnis wird aufbereitet. Dabei werden Links zu Volltexten verfolgt und die Volltexte besorgt. Die beschafften Dokumente werden mit einem Schlüsselwortretrieval auf Passagen untersucht, in denen sich Suchkonzepte aus der Frage / Ontologie häufen. Diese Passagen werden zum Zusammenfassen vorgeschlagen. In ihnen werden die Aussagen syntaktisch analysiert. Die Systemagenten untersuchen sie. Lassen Aussagen sich mit einer semantischen Relation an die Frage anbinden, tragen also zur deren Beantwortung bei, werden sie in die Zusammenfassung aufgenommen, es sei denn, andere Agenten machen Hinderungsgründe geltend, z.B. Redundanz. Das Ergebnis der Zusammenfassung wird in das Frage/Antwort-Szenario integriert. Präsentiert werden Exzerpte aus den Quelldokumenten. Mit einem Link vermitteln sie einen sofortigen Rückgriff auf die Quelle. SummIt-BMT ist zum nächsten Durchgang von Informationssuche und Zusammenfassung bereit, sobald der Benutzer dies wünscht.
-
Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
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- Date
- 22. 1.2016 12:29:41
-
Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003)
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- Date
- 27.11.2005 18:29:55
- Source
- Journal of information science. 29(2003) no.5, S.405-418
-
Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994)
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- Date
- 16. 8.1998 12:30:29
-
Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995)
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- Date
- 17. 8.1996 10:29:59
-
Goh, A.; Hui, S.C.: TES: a text extraction system (1996)
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- Date
- 26. 2.1997 10:22:43
-
Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996)
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- Date
- 6. 3.1997 16:22:15
-
Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996)
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- Source
- Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
-
Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998)
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- Date
- 17. 7.1996 14:16:29
-
Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007)
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- Abstract
- In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
-
Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008)
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- Date
- 29. 7.2008 19:35:12
-
Wang, S.; Koopman, R.: Embed first, then predict (2019)
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- Date
- 29. 9.2019 12:18:42
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Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006)
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- Date
- 22. 7.2006 17:25:48
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Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023)
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- Date
- 22. 1.2023 18:57:12
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Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023)
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- Date
- 22. 6.2023 14:55:20
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Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015)
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- Date
- 25. 1.2016 18:45:29
