-
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
-
Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005)
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-
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.
-
Automatic summarizing : introduction (1995)
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- Content
- Enthält u.a. Beiträge von: J. BATEMAN u. E. TEICH; R. BRANDOW, K. MITZE u. L.F. RAU; B. ENDRES-NIGGEMEYER, E. MAIER u. A. SIGEL; M.T. MAYBURY; K. McKEOWN, J. ROBIN u. K. KUKICH; A. ROTHKEGEL
- Editor
- Sparck Jones, K. u. B. Endres-Niggemeyer
-
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
-
McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995)
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-
Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P.: ¬The application of linguistic processing to automatic abstract generation (1993)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.538-552.
-
Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.478-483.
-
Marsh, E.: ¬A production rule system for message summarisation (1984)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.534-537.
-
Brandow, R.; Mitze, K.; Rau, L.F.: Automatic condensation of electronic publications by sentence selection (1995)
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-
Sparck Jones, K.; Endres-Niggemeyer, B.: Introduction: automatic summarizing (1995)
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-
Ahmad, K.: Text summarisation : the role of lexical cohesion analysis (1995)
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-
Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020)
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-
Goh, A.; Hui, S.C.: TES: a text extraction system (1996)
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- Date
- 26. 2.1997 10:22:43
-
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
-
Sparck Jones, K.: Automatic summarising : the state of the art (2007)
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-
Nomoto, T.: Discriminative sentence compression with conditional random fields (2007)
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- Abstract
- The paper focuses on a particular approach to automatic sentence compression which makes use of a discriminative sequence classifier known as Conditional Random Fields (CRF). We devise several features for CRF that allow it to incorporate information on nonlinear relations among words. Along with that, we address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF, drawing on techniques from biology and information retrieval. We also discuss a recursive application of CRF on the syntactic structure of a sentence as a way of improving the readability of the compression it generates. Experiments found that our approach works reasonably well compared to the state-of-the-art system [Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139, 91-107.].
-
Steinberger, J.; Poesio, M.; Kabadjov, M.A.; Jezek, K.: Two uses of anaphora resolution in summarization (2007)
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-
Endres-Niggemeyer, B.: ¬An empirical process model of abstracting (1992)
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- Source
- Mensch und Maschine: Informationelle Schnittstellen der Kommunikation. Proc. des 3. Int. Symposiums für Informationswissenschaft (ISI'92), 5.-7.11.1992 in Saarbrücken. Hrsg.: H.H. Zimmermann, H.-D. Luckhardt u. A. Schulz
-
Haag, M.: Automatic text summarization (2002)
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- Source
- Information - Wissenschaft und Praxis. 53(2002) H.4, 243-244
