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Semi-extractive Multi-document Summarization via Submodular Functions

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Statistical Language and Speech Processing (SLSP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9449))

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Abstract

In this paper, we propose an Extractive Maximum Coverage KnaPsack (MCKP) based model for query-based multi document summarization which integrates three monotone and submodular measures to detect importance of a sentence including Coverage, Relevance, and Compression. We apply an efficient scalable greedy algorithm to generate a summary which has a near optimal solution when its scoring functions are monotone nondecreasing and submodular. We use DUC 2007 dataset to evaluate our proposed method and the result shows improvement over two closely related works.

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Notes

  1. 1.

    The way we find the semantic similarity between two vectors of words, representing the sentence and query is inspired by the maximum weighted matching problem in a bipartite graph.

  2. 2.

    This sentence is from DUC 2007, topic D0701A.

  3. 3.

    http://duc.nist.gov/.

  4. 4.

    ROUGE package is available at http://www.berouge.com.

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Acknowledgments

Authors would like to thank Taylor Berg-Kirkpatrick [2] for kindly providing us valuable information and details of their work on compression features and also Giuseppe Pirro [25] for kindly providing us with their API for accessing WordNet.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Lethbridge, 4401 University Dr W, Lethbridge, Canada

    Fatemeh Ghiyafeh Davoodi & Yllias Chali

Authors
  1. Fatemeh Ghiyafeh Davoodi
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  2. Yllias Chali
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Correspondence to Fatemeh Ghiyafeh Davoodi .

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Editors and Affiliations

  1. Research Group on Mathematical Linguistic, Rovira i Virgili University, Tarragona, Spain

    Adrian-Horia Dediu

  2. Research Group on Mathematical Linguistic, Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary

    Klára Vicsi

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Ghiyafeh Davoodi, F., Chali, Y. (2015). Semi-extractive Multi-document Summarization via Submodular Functions. In: Dediu, AH., Martín-Vide, C., Vicsi, K. (eds) Statistical Language and Speech Processing. SLSP 2015. Lecture Notes in Computer Science(), vol 9449. Springer, Cham. https://doi.org/10.1007/978-3-319-25789-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-25789-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25788-4

  • Online ISBN: 978-3-319-25789-1

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