Search (10 results, page 1 of 1)

Endres-Niggemeyer, B.: SimSum : an empirically founded simulation of summarizing (2000) 0.01

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Date: 15. 8.2002 18:26:20

Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.01
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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.

Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.01

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Date: 22. 7.2006 17:25:48

Ou, S.; Khoo, C.S.G.; Goh, D.H.: Multi-document summarization of news articles using an event-based framework (2006) 0.01

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Date: 12. 3.2001 11:34:20
20. 1.2007 11:55:12

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) 0.01
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Abstract

Most knowledge accumulated through scientific discoveries in genomics and related biomedical disciplines is buried in the vast amount of biomedical literature. Since understanding gene regulations is fundamental to biomedical research, summarizing all the existing knowledge about a gene based on literature is highly desirable to help biologists digest the literature. In this paper, we present a study of methods for automatically generating gene summaries from biomedical literature. Unlike most existing work on automatic text summarization, in which the generated summary is often a list of extracted sentences, we propose to generate a semi-structured summary which consists of sentences covering specific semantic aspects of a gene. Such a semi-structured summary is more appropriate for describing genes and poses special challenges for automatic text summarization. We propose a two-stage approach to generate such a summary for a given gene - first retrieving articles about a gene and then extracting sentences for each specified semantic aspect. We address the issue of gene name variation in the first stage and propose several different methods for sentence extraction in the second stage. We evaluate the proposed methods using a test set with 20 genes. Experiment results show that the proposed methods can generate useful semi-structured gene summaries automatically from biomedical literature, and our proposed methods outperform general purpose summarization methods. Among all the proposed methods for sentence extraction, a probabilistic language modeling approach that models gene context performs the best.

Date

26.12.2007 16:34:20

Moens, M.F.; Dumortier, J.: Use of a text grammar for generating highlight abstracts of magazine articles (2000) 0.01

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Date: 10. 3.1997 20:31:13

Lee, J.-H.; Park, S.; Ahn, C.-M.; Kim, D.: Automatic generic document summarization based on non-negative matrix factorization (2009) 0.01

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Source: Information processing and management. 45(2009) no.1, S.20-34

Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005) 0.01

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Date: 25. 3.2005 19:20:11

Pinto, M.: Abstracting/abstract adaptation to digital environments : research trends (2003) 0.01

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Date: 10. 3.1997 20:31:13

Endres-Niggemeyer, B.; Ziegert, C.: SummIt-BMT : (Summarize It in BMT) in Diagnose und Therapie, Abschlussbericht (2002) 0.00

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Date: 9. 7.2011 20:00:41

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