Skip to main content
SpringerLink
Log in

A context-aware study for sentence ordering

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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. 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).

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. 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. Vapnik, V. (1982). Estimation of dependences based on empirical data. New York: Springer.

    Google Scholar 

  10. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.

    Book  Google Scholar 

  11. Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.

    Google Scholar 

  12. Fukunaga, K. (1990). Introduction to statistical patten recognition. San Diego: Academic Press.

    Google Scholar 

  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. Deng, J.-L. (2002). Grey Theory Base. Wuhan: Huazhong University of Science and Technology Press.

    Google Scholar 

  15. Deng, J. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.

    Article  Google Scholar 

  16. Liu, S.-F., & Xie, N.-M. (2008). The grey system theory and application (4rd edn.). Beijing: Science Press.

    Google Scholar 

  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. Lapata, M. (2002). Automatic Evaluation of Information Ordering: Kendall’s Tau. Association for Computational Linguistics, pp. 471–484.

  19. Sogou Labs (2011). http://www.sogou.com/labs/dl/t.html.

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science and Technology, Wuhan University, Wuhan, China

    Gongfu Peng & Yanxiang He

  2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Naixue Xiong

  3. Electronic Documentation Division, Korean Intellectual Property Office, Seoul, Korea

    Soocheol Lee

  4. School of Electrical Engineering, Korea University, Seoul, Korea

    Seungmin Rho

Authors
  1. Gongfu Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanxiang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naixue Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soocheol Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seungmin Rho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naixue Xiong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Peng, G., He, Y., Xiong, N. et al. A context-aware study for sentence ordering. Telecommun Syst 52, 1343–1351 (2013). https://doi.org/10.1007/s11235-011-9647-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-011-9647-5

Keywords

Search

Navigation