Text Summarization of Single Documents Based on Syntactic Sequences

  • Paul Villavicencio
  • Toyohide Watanabe
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 11)


In this paper we propose a summarization method for scientific articles from the viewpoint of the syntactic sequences. The objective is to generate an extractive summary by ranking sentences according to their informative content, on the basis of the idea that the writing styles of authors create syntactic patterns which may contain important information about topics explained in a research paper. We use two main document features in our summarizing algorithm: syntactic sequences and frequent terms per section. We present an evaluation of our proposed algorithm by comparing it with existing summarization methods.


text summarization text analysis parts of speech 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Proceedings of the Intelligent Scalable Text Summarization Workshop, Madrid, pp. 10–17 (1997)Google Scholar
  2. 2.
    Bawakid, A., Oussalah, M.: A semantic summarization system: University of Birmingham at TAC 2008. In: Proceedings of the First Text Analysis Conference, Maryland, USA (2008)Google Scholar
  3. 3.
    Hercules Dalianis. SweSum - A Text Summarizer for Swedish. Technical report, NADA, KTH, Stockholm (2000),
  4. 4.
    Hunston, S.: Starting with the small words Patterns, lexis and semantic sequences. International journal of corpus linguistics 13(3), 271–295 (2008)CrossRefGoogle Scholar
  5. 5.
    Gledhill, C.J.: Collocations in science writing. Narr Verlag, Tübingen (2000)Google Scholar
  6. 6.
    Lioma, C., Ounis, I.: A syntactically-based query reformulation technique for information retrieval. Information Processing and Management: an International Journal 44(1), 143–162 (2008)MATHCrossRefGoogle Scholar
  7. 7.
    Liu, X., Webster, J.J., Kit, C.: An Extractive Text Summarizer Based on Significant Words. In: Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy, pp. 168–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools 13(1), 157–169 (2004)CrossRefGoogle Scholar
  9. 9.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics, Stroudsburg (2002)Google Scholar
  10. 10.
    Radev, D., Allison, T., Blair-Goldensohn, S., Blitzer, J., Çelebi, A., Dimitrov, S., Drabek, E., Hakim, A., Lam, W., Liu, D., Otterbacher, J., Qi, H., Saggion, H., Teufel, S., Topper, M., Winkel, A., Zhang, Z.: MEAD - a platform for multidocument multilingual text summarization. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon, Portugal (2004)Google Scholar
  11. 11.
    Nadav Rotem. Open Text Summarizer,
  12. 12.
    Saggion, H., Lapalme, G.: Generating indicative-informative summaries with sumUM. Computational Linguistics 28(4), 497–526 (2002)CrossRefGoogle Scholar
  13. 13.
    Schuemie, M.J., Weeber, M., Schijvenaars, B.A., van Mulligen, E.M., Christiaan, C., van der Eijk, Jelier, R., Mons, B., Kors, J.A.: Distribution of information in biomedical abstracts and full-text publications.. Bioinformatics 20(16), 2597–2604 (2004)CrossRefGoogle Scholar
  14. 14.
    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. ACM Press, New York (2000)CrossRefGoogle Scholar
  15. 15.
    Strobelt, H., Oelke, D., Rohrdantz, C., Stoffel, A., Keim, D., Deussen, O.: Document Cards: A Top Trumps Visualization for Documents. IEEE Transactions on Visualization and Computer Graphics 15(6), 1145–1152 (2009)CrossRefGoogle Scholar
  16. 16.
    Teufel, S., Moens, M.: Summarizing scientific articles: experiments with relevance and rhetorical status. Computational Linguistics 28(4), 409–445 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paul Villavicencio
    • 1
  • Toyohide Watanabe
    • 1
  1. 1.Department of Systems and Social Informatics, Graduate School of Information ScienceNagoya UniversityJapan

Personalised recommendations