Query-Oriented Summarization Based on Neighborhood Graph Model

  • Furu Wei
  • Yanxiang He
  • Wenjie Li
  • Lei Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)


In this paper, we investigate how to combine the link-aware and link-free information in sentence ranking for query-oriented summarization. Although the link structure has been emphasized in the existing graph-based summarization models, there is lack of pertinent analysis on how to use the links. By contrasting the text graph with the web graph, we propose to evaluate significance of sentences based on neighborhood graph model. Taking the advantage of the link information provided on the graph, each sentence is evaluated according to its own value as well as the cumulative impacts from its neighbors. For a task like query-oriented summarization, it is critical to explore how to reflect the influence of the query. To better incorporate query information into the model, we further design a query-sensitive similarity measure to estimate the association between a pair of sentences. When evaluated on DUC 2005 dataset, the results of the pro-posed approach are promising.


Query-Oriented Summarization Neighborhood Graph Query-Sensitive Similarity 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Furu Wei
    • 1
    • 2
  • Yanxiang He
    • 1
  • Wenjie Li
    • 2
  • Lei Huang
    • 1
  1. 1.Department of Computer Science and TechnologyWuhan UniversityChina
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong

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