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Many are better than one: improving multi-document summarization via weighted consensus

Published: 19 July 2010 Publication History

Abstract

Given a collection of documents, various multi-document summarization methods have been proposed to generate a short summary. However, few studies have been reported on aggregating different summarization methods to possibly generate better summarization results. We propose a weighted consensus summarization method to combine the results from single summarization systems. Experimental results on DUC2004 data sets demonstrate the performance improvement by aggregating multiple summarization systems, and our proposed weighted consensus summarization method outperforms other combination methods.

References

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D. Lee and H. Seung. Learning the parts of objects by non-negative matrix factorization. In Nature, pages 788--791, 1999.
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T. Li and C. Ding. Weighted consensus clustering. In SIAM Data Mining 2008.
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C.-Y. Lin and E. Hovy. Automatic evaluation of summaries using n-gram co-occurrence statistics. In NLT-NAACL 2003.
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D. Radev, H. Jing, M. Stys, and D. Tam. Centroid-based summarization of multiple documents. In Information Processing and Management, pages 919--938, 2004.
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C. Spearman. The proof and measurement of association between two things. In Amer. J. Psychol, 1904.
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V. Thapar, A. A. Mohamed, and S. Rajasekaran. A consensus text summarizer based on meta-search algorithms. In Proceedings of 2006 IEEE International Symposium on Signal Processing and Information Technology, 2006.

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  1. Many are better than one: improving multi-document summarization via weighted consensus

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 19 July 2010

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    Author Tags

    1. summarization
    2. weighted consensus

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2022)One-to-many comparative summarization for patentsScientometrics10.1007/s11192-022-04307-8127:4(1969-1993)Online publication date: 1-Apr-2022
    • (2013)Comparative Document Summarization via Discriminative Sentence SelectionACM Transactions on Knowledge Discovery from Data10.1145/2435209.24352117:1(1-18)Online publication date: 1-Mar-2013
    • (2012)Comparative document summarization via discriminative sentence selectionACM Transactions on Knowledge Discovery from Data10.1145/2362383.23623866:3(1-18)Online publication date: 29-Oct-2012
    • (2012)Weighted consensus multi-document summarizationInformation Processing and Management: an International Journal10.1016/j.ipm.2011.07.00348:3(513-523)Online publication date: 1-May-2012
    • (2012)A zipf-like distant supervision approach for multi-document summarization using wikinews articlesProceedings of the 19th international conference on String Processing and Information Retrieval10.1007/978-3-642-34109-0_15(143-154)Online publication date: 21-Oct-2012
    • (2010)Multi-Document Summarization Model Based on Integer Linear ProgrammingIntelligent Control and Automation10.4236/ica.2010.1201201:02(105-111)Online publication date: 2010

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