A document-sensitive graph model for multi-document summarization

  • Authors:

  • Furu Wei;Wenjie Li;Qin Lu;Yanxiang He

  • Affiliations:

  • The Hong Kong Polytechnic University, Department of Computing, Kowloon, Hong Kong and Wuhan University, Department of Computer Science and Technology, Wuhan, China;The Hong Kong Polytechnic University, Department of Computing, Kowloon, Hong Kong;The Hong Kong Polytechnic University, Department of Computing, Kowloon, Hong Kong;Wuhan University, Department of Computer Science and Technology, Wuhan, China

  • Venue:
  • Knowledge and Information Systems
  • Year:
  • 2010

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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.