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)

Abstract

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.

Keywords

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

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References

  1. 1.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)MATHGoogle Scholar
  2. 2.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR 1998, pp. 335–336 (1998)Google Scholar
  3. 3.
    DUC. Document Understanding Conference, http://www-nlpir.nist.gov/projects/duc/intro.html
  4. 4.
    Griffiths, T., Steyvers, M.: Finding Scientific Topics. Proceedings of the National Academy of Sciences 101(suppl.1), 5228–5235 (2004)CrossRefGoogle Scholar
  5. 5.
    Lin, C.Y., Hovy, E.H.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of HLT-NAACL 2003, pp. 71–78 (2003)Google Scholar
  6. 6.
    Otterbacher, J., Erkan, G., Radev, D.R., Mihalcea, R.: Using random walks for question-focused sentence retrieval. In: Proceedings of HLT-EMNLP 2005, pp. 915–922 (2005)Google Scholar
  7. 7.
    Wan, X., Yang, J.: Multi-document summarization using cluster-based link analysis. In: Proceedings of the 31st ACM SIGIR, pp. 299–306 (2008)Google Scholar
  8. 8.
    Zha, H.: Generic Summarization and Key Phrase Extraction using Mutual Reinforcement Principle and Sentence Clustering. In: Proceedings of the 25th ACM SIGIR 2002, pp. 113–120 (2002)Google Scholar
  9. 9.
    Brin, S., Page, L.: The anatomy of a large scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  10. 10.
    Kleinberg, J.M.: Authoritative sources in hyperlinked environment. Journal of ACM 46(5), 604–632 (1999)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Erkan, G., Radev, D.R.: LexRank: Graph-based centrality as salience in text summarization. Journal of Artificial Intelligence Research 22, 457–479 (2004)Google Scholar
  12. 12.
    Lin, C., Hovy, E.: From single to multi-document summarization: A prototype system and its evaluation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (2002)Google Scholar
  13. 13.
    Mihalcea, R.: Graph-based ranking algorithms for sentence extraction, applied to text summarization. In: Proceedings of ACL 2004 (2004)Google Scholar
  14. 14.
    Mihalcea, R.: Language independent extractive summarization. In: Proceedings of ACL (2005)Google Scholar
  15. 15.
    Radev, D.R., Jing, H., Stys, M., Tam, D.: Centroid-based summarization of multiple documents. Information Processing & Management, 919–938 (2004)Google Scholar
  16. 16.
    Cai, X., Li, W., Ouyang, Y., et al.: Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization. In: Proceedings of Coling 2010, pp. 134–142 (2010)Google Scholar

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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