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VisualSum: an interactive multi-document summarizationsystem using visualization

Published: 19 July 2010 Publication History

Abstract

Given a collection of documents, most of existing multidocument summarization methods automatically generate a static summary for all the users. However, different users may have different opinions on the documents, thus there is a necessity for improving users' interactions in the summarization process. In this paper, we propose an interactive document summarization system using information visualization techniques.

References

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Y. Gong and X. Liu. Generic text summarization using relevance measure and latent semantic analysis. SIGIR, 2001.
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D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. NIPS, 2001.
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C.-Y. Lin and E. Hovy. Automatic evaluation of summaries using n-gram co-occurrence statistics. Association for Computational Linguistics, pages 71--78, 2003.
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S. Liu, M. X. Zhou, S. Pan, W. Qian, W. Cai, and X. Lian. Interactive, topic-based visual text summarization and analysis. CIKM, 2009.
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A. Noack. Modularity clustering is force-direced layout. Physical Review E, 79:026102, 2009.

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  1. VisualSum: an interactive multi-document summarizationsystem using visualization

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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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    Published: 19 July 2010

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    1. multi-document summarization
    2. visualization

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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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