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Moens, M.-F.; Uyttendaele, C.; Dumotier, J.: Abstracting of legal cases : the potential of clustering based on the selection of representative objects (1999)
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Liang, S.-F.; Devlin, S.; Tait, J.: Investigating sentence weighting components for automatic summarisation (2007)
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Steinberger, J.; Poesio, M.; Kabadjov, M.A.; Jezek, K.: Two uses of anaphora resolution in summarization (2007)
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Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016)
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Ling, X.; Jiang, J.; He, X.; Mei, Q.; Zhai, C.; Schatz, B.: Generating gene summaries from biomedical literature : a study of semi-structured summarization (2007)
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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
-
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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Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010)
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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.
-
Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009)
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Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010)
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Cai, X.; Li, W.: Enhancing sentence-level clustering with integrated and interactive frameworks for theme-based summarization (2011)
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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
-
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
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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
