Sentence Ordering in Extractive MDS

  • Zengchang Zhang
  • Dexi Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Ordering information is a critical task for multi-document summarization(MDS) because it heavily influent the coherence of the generated summary. In this paper, we propose a hybrid model for sentence ordering in extractive multi-document summarization that combines four relations between sentences – chronological relation, positional relation, topical relation and dependent relation. This model regards sentence as vertex and combined relation as edge of a directed graph on which the approximately optimal ordering can be generated with PageRank analysis. Evaluation of our hybrid model shows a significant improvement of the ordering over strategies losing some relations and the results also indicate that this hybrid model is robust for articles with different genre.


Hybrid Model Dependent Relation Vertex Versus Text Structure Combine Relation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Mani, I.: Automatic Summarization. John Benjamins, Amsterdam (2001)MATHGoogle Scholar
  2. Radev, D.R., Hovy, E.H., McKeown, K.: Introduction to the Special Issue on Summarization. Computational Linguistics 28(4), 399–408 (2002)CrossRefGoogle Scholar
  3. Okazaki, N., Matsuo, Y., Ishizuka, M.: Coherent Arrangement of Sentences Extracted from Multiple Newspaper Articles. In: PRICAI, pp. 882–891 (2004)Google Scholar
  4. Barzilay, R., Elhadad, E., McKeown, K.: Inferring strategies for sentence ordering in multidocument summarization. Journal of Artificial Intelligence Research 17, 35–55 (2002)MATHGoogle Scholar
  5. Lapata, M.: Probabilistic text structuring: experiments with sentence ordering. In: Proceedings of the 41st Meeting of the Association of Computational Linguistics, pp. 545–552 (2003)Google Scholar
  6. Asher, N., Lascarides, A.: Logics of Conversation. Cambridge University Press, Cambridge (2003)Google Scholar
  7. Grosz, B., Joshi, A., Weinstein, S.: Centering: A framework for modeling the local coherence of discourse. Computational Linguistics 21(2), 203–225 (1995)Google Scholar
  8. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30, 1–7 (1998)CrossRefGoogle Scholar
  9. Paul, O., James, Y.: An Introduction to DUC-2004. In: Proceedings of the 4th Document Understanding Conference, DUC 2004 (2004)Google Scholar
  10. Lin, C.Y., Hovy, E.: Automatic Evaluation of Summaries Using N-gram Co-Occurrence Statistics. In: Proceedings of the Human Technology Conference (HLTNAACL-2003), Edmonton, Canada (2003)Google Scholar
  11. Lebanon, G., Lafferty, J.: Combining rankings using conditional probability models on permutations. In: Proceedings of the 19th International Conference on Machine Learning, pp. 363–370. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zengchang Zhang
    • 1
  • Dexi Liu
    • 1
    • 2
  1. 1.School of PhysicsXiangfan UniversityXiangfanP.R. China
  2. 2.School of ComputerWuhan UniversityWuhanP.R. China

Personalised recommendations