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A document-sensitive graph model for multi-document summarization

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Abstract

In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document–document and document–sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks.

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Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Furu Wei, Wenjie Li & Qin Lu

  2. Department of Computer Science and Technology, Wuhan University, Wuhan, China

    Furu Wei & Yanxiang He

Authors
  1. Furu Wei
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  2. Wenjie Li
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  3. Qin Lu
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  4. Yanxiang He
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Correspondence to Wenjie Li.

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Wei, F., Li, W., Lu, Q. et al. A document-sensitive graph model for multi-document summarization. Knowl Inf Syst 22, 245–259 (2010). https://doi.org/10.1007/s10115-009-0194-2

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  • DOI: https://doi.org/10.1007/s10115-009-0194-2

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