LDA-Based Topic Formation and Topic-Sentence Reinforcement for Graph-Based Multi-document Summarization

  • Dehong Gao
  • Wenjie Li
  • You Ouyang
  • Renxian Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7675)


In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.


Multi-document summarization Graph-based sentence ranking Latent Dirichlet Allocation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dehong Gao
    • 1
  • Wenjie Li
    • 1
  • You Ouyang
    • 2
  • Renxian Zhang
    • 1
  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong
  2. 2.Miaozhen SystemsBeijingChina

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