Extractive Summarization Based on Event Term Temporal Relation Graph and Critical Chain

  • Maofu Liu
  • Wenjie Li
  • Huijun Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)


In this paper, we investigate whether temporal relations among event terms can help improve event-based extractive summarization and text cohesion of machine-generated summaries. Using the verb semantic relation, namely happens-before provided by VerbOcean, we construct an event term temporal relation graph for source documents. We assume that the maximal weakly connected component on this graph represents the main topic of source documents. The event terms in the temporal critical chain identified from the maximal weakly connected component are then used to calculate the significance of the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that extractive summarization based on event term temporal relation graph and critical chain is able to organize final summaries in a more coherent way and accordingly achieves encouraging improvement over the well-known tf*idf-based and PageRank-based approaches.


Event Term Source Document Source Vertex Text Cohesion Final Summary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    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
  2. 2.
    Filatova, E., Hatzivassiloglou, V.: Event-based Extractive Summarization. In: Proceedings of ACL 2004 Workshop on Summarization, pp. 104–111 (2004)Google Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Afantenos, S.D., Karkaletsis, V., Stamatopoulos, P.: Summarizing Reports on Evolving Events; Part I: Linear Evolution. In: Proceedings of Recent Advances in Natural Language Processing (2005)Google Scholar
  6. 6.
    Wu, M., Li, W., Lu, Q., Wong, K.F.: Event-Based Summarization Using Time Features. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 563–574. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Jatowt, A., Ishizuka, M.: Temporal Web Page Summarization. In: Proceedings of the 5th International Conference on Web Information Systems Engineering, pp. 303–312 (2004)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Silber, H.G., McCoy, K.F.: Efficient Text Summarization Using Lexical Chains. In: Proceedings of the 5th International Conference on Intelligent User Interfaces, pp. 252–255 (2000)Google Scholar
  11. 11.
    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
  12. 12.
    Doran, W., Stokes, N., Dunnion, J., Carthy, J.: Assessing the Impact of Lexical Chain Scoring Methods and Sentence Extraction Schemes on Summarization. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 627–635. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Zhou, Q., Sun, L., Lv, Y.: ISCAS at DUC 2006. In: Proceedings of Document Understanding Conference 2006 (2006)Google Scholar
  14. 14.
    Reeve, L.H., Han, H., Brooks, A.D.: BioChain-Lexical Chaining Methods for Biomedical Text Summarization. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 180–184 (2006)Google Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Lin, C.Y., Hovy, E.: Automatic Evaluation of Summaries using N-gram Cooccurrence Statistics. In: Proceedings of HLTNAACL, pp. 71–78 (2003)Google Scholar
  18. 18.
    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
  • Wenjie Li
    • 2
  • Huijun Hu
    • 1
  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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