Terms Derived from Frequent Sequences for Extractive Text Summarization

  • Yulia Ledeneva
  • Alexander Gelbukh
  • René Arnulfo García-Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4919)


Automatic text summarization helps the user to quickly understand large volumes of information. We present a language- and domain-independent statistical-based method for single-document extractive summarization, i.e., to produce a text summary by extracting some sentences from the given text. We show experimentally that words that are parts of bigrams that repeat more than once in the text are good terms to describe the text’s contents, and so are also so-called maximal frequent sentences. We also show that the frequency of the term as term weight gives good results (while we only count the occurrences of a term in repeating bigrams).


Term Selection News Report Term Weighting Formal Concept Analysis Frequent Sequence 
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.
    Lin, C.Y., Hovy, E.: Automated Text Summarization in SUMMARIST. In: Proc. of ACL Workshop on Intelligent, Scalable Text Summarization, Madrid, Spain (1997)Google Scholar
  2. 2.
    Kupiec, J., Pedersen, J.O., Chen, F.: A Trainable Document Summarizer. In: Proceedings of the 18th ACM-SIGIR Conference on Research and Development in Information Retrieval, Seattle, pp. 68–73 (1995)Google Scholar
  3. 3.
    Song, Y., et al.: A Term Weighting Method based on Lexical Chain for Automatic Summarization. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Cristea, D., et al.: Summarization through Discourse Structure. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Liu, D., et al.: Multi-Document Summarization Based on BE-Vector Clustering. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Xu, W., Li, W., et al.: Deriving Event Relevance from the Ontology Constructed with Formal Concept Analysis. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Villatoro-Tello, E., Villaseñor-Pineda, L., Montes-y-Gómez, M.: Using Word Sequences for Text Summarization. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2006. LNCS (LNAI), vol. 4188, pp. 293–300. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Chuang, T.W., Yang, J.: Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Proc. of the ACL 2004 Workshop, Barcelona, España (2004)Google Scholar
  9. 9.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information processing & Management 24, 513–523 (1988)CrossRefGoogle Scholar
  10. 10.
    García-Hernández, R.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 478–486. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    García-Hernández, R.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A New Algorithm for Fast Discovery of Maximal Sequential Patterns in a Document Collection. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 514–523. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    DUC. Document understanding conference 2002 (2002),
  13. 13.
    Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of Workshop on Text Summarization of ACL, Spain (2004)Google Scholar
  14. 14.
    Lin, C.Y., Hovy, E.: Automatic Evaluation of Summaries Using N-gram Co-Occurrence Statistics. In: Proceedings of HLT-NAACL, Canada (2003)Google Scholar
  15. 15.
    Mihalcea, R.: Random Walks on Text Structures. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 249–262. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Mihalcea, R., Tarau, P.: TextRank: Bringing Order into Texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain (2004)Google Scholar
  17. 17.
    Hassan, S., Mihalcea, R., Banea, C.: Random-Walk Term Weighting for Improved Text Classification. In: Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2007), Irvine, CA (2007)Google Scholar
  18. 18.
    HaCohen-Kerner, Y., Zuriel, G., Asaf, M.: Automatic Extraction and Learning of Keyphrases from Scientific Articles. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 645–657. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Bolshakov, I.A.: Getting One’s First Million...Collocations. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 229–242. Springer, Heidelberg (2004)Google Scholar
  20. 20.
    Koster, C.H.A.: Transducing Text to Multiword Units. In: Workshop on Multiword Units MEMURA at the fourth International Conference on Language Resources and Evaluation, LREC-2004, Lisbon, Portugal (2004)Google Scholar
  21. 21.
    Baeza Yates, R., Ribeiro Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yulia Ledeneva
    • 1
  • Alexander Gelbukh
    • 1
  • René Arnulfo García-Hernández
    • 2
  1. 1.Natural Language and Text Processing Laboratory, Center for Computing ResearchNational Polytechnic InstituteMexico
  2. 2.Instituto Tecnologico de TolucaMexico

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