Event-Based Summarization Using Critical Temporal Event Term Chain

  • Maofu Liu
  • Wenjie Li
  • Xiaolong Zhang
  • Ji Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)


In this paper, we investigate whether temporal relations among event terms can help improve event-based summarization and text cohesion of final summaries. By connecting event terms with happens-before relations, we build a temporal event term graph for source documents. The event terms in the critical temporal event term chain identified from the maximal weakly connected component are used to evaluate the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that event-based summarization using the critical temporal event term chain is able to organize final summaries in a more coherent way and make improvement over the well-known tf*idf-based and PageRank-based summarization approaches.


Event-Based Summarization Event Term Graph Temporal Event Term Chain Depth-First Search Algorithm 


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  1. 1.
    Timothy, C., Patrick, P.: VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (2004)Google Scholar
  2. 2.
    Daniel, N., Radev, D., Allison, T.: Sub-event based Multi-document Summarization. In: Proceedings of the HLT-NAACL Workshop on Text Summarization, pp. 9–16 (2003)Google Scholar
  3. 3.
    Filatova, E., Hatzivassiloglou, V.: Event-based Extractive Summarization. In: Proceedings of ACL 2004 Workshop on Summarization, pp. 104–111 (2004)Google Scholar
  4. 4.
    Allan, J., Gupta, R., Khandelwal, V.: Temporal Summaries of News Topics. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 10–18 (2001)Google Scholar
  5. 5.
    Lim, J.M., Kang, I.S., Bae, J.H., Lee, J.H.: Sentence Extraction Using Time Features in Multi-document Summarization. In: Information Retrieval Technology: Asia Information Retrieval Symposium (2004)Google Scholar
  6. 6.
    Morris, J., Hirst, G.: Lexical Cohesion Computed by Thesaurus Relations as an Indicator of the Structure of Text. Computational Linguistics 17(1), 21–48 (1991)Google Scholar
  7. 7.
    Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Proceedings of ACL 1997/EACL 1997 Workshop on Intelligent Scalable Text Summarization, pp. 10–17 (1997)Google Scholar
  8. 8.
    Silber, H.G., McCoy, K.F.: Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization. Computational Linguistics 28(4), 487–496 (2002)CrossRefGoogle Scholar
  9. 9.
    Reeve, L.H., Han, H., Brooks, A.D.: The Use of Domain-Specific Concepts in Biomedical Text Summarization. Information Processing and Management 43(6), 1765–1776 (2007)CrossRefGoogle Scholar
  10. 10.
    Lin, C.Y., Hovy, E.: Automatic Evaluation of Summaries using N-gram Cooccurrence Statistics. In: Proceedings of HLTNAACL, pp. 71–78 (2003)Google Scholar
  11. 11.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank CitationRanking: Bring Order to the Web. Technical Report, Stanford University (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maofu Liu
    • 1
    • 2
  • Wenjie Li
    • 2
  • Xiaolong Zhang
    • 1
  • Ji Zhang
    • 2
  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanP.R. China
  2. 2.Department of ComputingThe Hong Kong Polytechnic University, KowloonHong Kong

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