Telecommunication Systems

, Volume 52, Issue 2, pp 1343–1351 | Cite as

A context-aware study for sentence ordering

  • Gongfu Peng
  • Yanxiang He
  • Naixue Xiong
  • Soocheol Lee
  • Seungmin Rho


This paper proposes a context-aware method for sentence ordering in multi-document summarization task, which combines support vector machine (SVM) and Grey Model (GM). Multi-Documents summarization task focus on how to extract main information of document set, this paper aim to prove the coherence of summary based on the context of document set. Firstly, the method trains the SVM with sentences of each source document and predict sentences sequence of summary as primary dataset. Secondly, using Grey Model to process the primary dataset, according to the analysis we achieve the final sequence of summary sentences. Experiments on 100 summaries shown this method provide a much higher precision than probabilistic model in sentence ordering task.


Context-aware Sentence ordering Multi-document 


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  1. 1.
    Barzilay, R., Elhadad, N., & McKeown, K. (2002). Inferring strategies for sentence ordering in multidocument news summarization. The Journal of Artificial Intelligence Research, 17, 35–55. Google Scholar
  2. 2.
    Madnani, N., Passonneau, R., Ayan, N. F., Conroy, J., Dorr, B., Klavans, J., O’Leary, D., & Schlesinger, J. (2007). Measuring variability in sentence ordering for news summarization. In Proceedings of the 11th European workshop on natural language generation, Schloss Dagstuhl, Germany, 17–20 June 2007 (pp. 81–88). CrossRefGoogle Scholar
  3. 3.
    Okazaki, N., Matsuo, Y., & Ishizuka, M. (2004). Improving chronological sentence ordering by precedence relation. In Proceedings of 20th international conference on computational linguistics (COLING), 2004 (pp. 750–756). Google Scholar
  4. 4.
    Lapata, M. (2003). Probabilistic text structuring: Experiments with sentence ordering. In Proceedings of the annual meeting of ACL, 2003 (pp. 545–552). Google Scholar
  5. 5.
    Barzilay, R., & Lee, L. (2004). Catching the drift: Probabilistic content models, with applications to generation and summarization. In HLT-NAACL 2004: proceedings of the main conference, 2004 (pp. 113–120). Google Scholar
  6. 6.
    Grosz, B., Joshi, A. K., & Weinstein, S. (1995). Centering: a framework for modeling the local coherence of discourse. Computational Linguistics, 21(2), 203–225. Google Scholar
  7. 7.
    Barzilay, R., Elhadad, N., & McKeown, K. R. (2001). Sentence ordering in multi-document summarization. In Proceedings of the 1st human language technology conference, 2001 (pp. 1–7). Google Scholar
  8. 8.
    Donghong, J., & Yu, N. (2008). Sentence ordering based on cluster adjacency in multi-document summarization. In The third international joint conference on natural language processing, 2008 (pp. 745–750). Google Scholar
  9. 9.
    Vapnik, V. (1982). Estimation of dependences based on empirical data. New York: Springer. Google Scholar
  10. 10.
    Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer. CrossRefGoogle Scholar
  11. 11.
    Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill. Google Scholar
  12. 12.
    Fukunaga, K. (1990). Introduction to statistical patten recognition. San Diego: Academic Press. Google Scholar
  13. 13.
    Ye, J. (2008). Generalized linear discriminant analysis: a unified framework and efficient model selection. IEEE Transactions on Neural Networks, 19(10), 1770. Google Scholar
  14. 14.
    Deng, J.-L. (2002). Grey Theory Base. Wuhan: Huazhong University of Science and Technology Press. Google Scholar
  15. 15.
    Deng, J. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294. CrossRefGoogle Scholar
  16. 16.
    Liu, S.-F., & Xie, N.-M. (2008). The grey system theory and application (4rd edn.). Beijing: Science Press. Google Scholar
  17. 17.
    Lebanon, G., & Lafferty, J. (2002). Combining rankings using conditional probability models on permutations. In Proceedings of the 19th international conference on machine learning. Google Scholar
  18. 18.
    Lapata, M. (2002). Automatic Evaluation of Information Ordering: Kendall’s Tau. Association for Computational Linguistics, pp. 471–484. Google Scholar
  19. 19.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Gongfu Peng
    • 1
  • Yanxiang He
    • 1
  • Naixue Xiong
    • 2
  • Soocheol Lee
    • 3
  • Seungmin Rho
    • 4
  1. 1.Dept. of Computer Science and TechnologyWuhan UniversityWuhanChina
  2. 2.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  3. 3.Electronic Documentation DivisionKorean Intellectual Property OfficeSeoulKorea
  4. 4.School of Electrical EngineeringKorea UniversitySeoulKorea

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