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A light-weight summarizer based on language model with relative entropy

Published: 08 March 2009 Publication History

Abstract

A new method for sentence extraction on the basis of language model with relative entropy is presented in this paper. The proposed technique first builds a sentence language model and document cluster language model respectively for the sentence and the documents. The sentences are then ranked according to the relative entropies of the estimated document language model with respect to the estimated sentence language model. The overall results on DUC and MSE corpus demonstrate that the proposed approach outperforms some of the best reported results for generic multi-document summarization.

References

[1]
Cover, T. M., and Thomas, J. A. 1991 Elements of Information Theory. Wiley-Interscience, New York, New York, 1991.
[2]
C. Y. Lin and E. H. Hovy 2003 Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics. In Proceedings of HLT-NAACL 2003
[3]
Inderjeet Mani and Mark Maybury 1999 Advances in Automatic Text Summarization. MIT Press, 1999.
[4]
V. Lavrenko and W. B. Croft 2001 Relevance based language models. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 2001.

Cited By

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  • (2022)A Novel Modified Harmonic Mean Combined with Cohesion Score for Multi-document SummarizationDistributed Computing and Intelligent Technology10.1007/978-3-030-94876-4_16(227-244)Online publication date: 17-Jan-2022
  • (2011)Sentence ranking for document indexingProceedings of the 4th international conference on Pattern recognition and machine intelligence10.5555/2026851.2026901(274-279)Online publication date: 27-Jun-2011
  • (2011)Sentence Ranking for Document IndexingPattern Recognition and Machine Intelligence10.1007/978-3-642-21786-9_45(274-279)Online publication date: 2011

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  1. A light-weight summarizer based on language model with relative entropy

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    cover image ACM Conferences
    SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
    March 2009
    2347 pages
    ISBN:9781605581668
    DOI:10.1145/1529282
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 08 March 2009

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    Author Tags

    1. multi-document summarization
    2. relative entropy
    3. sentence extraction

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    SAC09
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    SAC09: The 2009 ACM Symposium on Applied Computing
    March 8, 2009 - March 12, 2008
    Hawaii, Honolulu

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    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2022)A Novel Modified Harmonic Mean Combined with Cohesion Score for Multi-document SummarizationDistributed Computing and Intelligent Technology10.1007/978-3-030-94876-4_16(227-244)Online publication date: 17-Jan-2022
    • (2011)Sentence ranking for document indexingProceedings of the 4th international conference on Pattern recognition and machine intelligence10.5555/2026851.2026901(274-279)Online publication date: 27-Jun-2011
    • (2011)Sentence Ranking for Document IndexingPattern Recognition and Machine Intelligence10.1007/978-3-642-21786-9_45(274-279)Online publication date: 2011

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