Search (15 results, page 1 of 1)

Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.01

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Date: 22. 1.2016 12:29:41

Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003) 0.01

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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) 0.01

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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) 0.01

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Date: 17. 8.1996 10:29:59

Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01

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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) 0.01

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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) 0.01

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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) 0.00

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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) 0.00
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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) 0.00

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Date: 29. 7.2008 19:35:12

Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.00

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Date: 29. 9.2019 12:18:42

Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00

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Date: 22. 7.2006 17:25:48

Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.00

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Date: 22. 1.2023 18:57:12

Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00

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Date: 22. 6.2023 14:55:20

Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015) 0.00

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Date: 25. 1.2016 18:45:29

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