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Single Extractive Text Summarization Based on a Genetic Algorithm

  • René Arnulfo García-Hernández
  • Yulia Ledeneva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)

Abstract

Extractive text summarization consists in selecting the most important units (normally sentences) from the original text, but it must be done as closer as humans do. Several interesting automatic approaches are proposed for this task, but some of them are focused on getting a better result rather than giving some assumptions about what humans use when producing a summary. In this research, not only the competitive results are obtained but also some assumptions are given about what humans tried to represent in a summary. To reach this objective a genetic algorithm is proposed with special emphasis on the fitness function which permits to contribute with some conclusions.

References

  1. 1.
    Lee, J.-H., Park, S., Ahn, C.-M., Kim, D.: Automatic Generic Document Summarization Based on Non-negative Matrix Factorization. Information Processing and Management 45, 20–34 (2009)CrossRefGoogle Scholar
  2. 2.
    Luhn, H.P.: The automatic creation of Literature abstracts. IBM Journal of Research and Development (1958)Google Scholar
  3. 3.
    Garcia-Hernandez, R.A., Montiel, R., Ledeneva, Y., Rendon, E., Gelbukh, A., Cruz, R.: Text Summarization by Sentence Extraction Using Unsupervised Learning. In: Orejas, F., Ehrig, H., Jantke, K.P., Reichel, H. (eds.) Abstract Data Types 1990. LNCS (LNAI), vol. 534, pp. 133–143. Springer, Heidelberg (1991)Google Scholar
  4. 4.
    Edmondson, H.P.: New Methods in Automatic Extraction. Journal of the Association for Computing Machinery (1969)Google Scholar
  5. 5.
    Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: SIGIR 1995 (1995)Google Scholar
  6. 6.
    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
  7. 7.
    Chuang, T., Yang, J.: Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Proc. of the ACL 2004 Workshop, Barcelona, España (2004)Google Scholar
  8. 8.
    Ledeneva, Y.: PhD. Thesis: Automatic Language-Independent Detection of Multiword Descriptions for Text Summarization. National Polytechnic Institute, Mexico (2009)Google Scholar
  9. 9.
    Ledeneva, Y.N., Gelbukh, A., García-Hernández, R.A.: Terms Derived from Frequent Sequences for Extractive Text Summarization. In: Gelbukh, A. (ed.) CICLing 2008. LNCS, vol. 4919, pp. 593–604. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Garcia-Hernandez, R.A., Martinez-Trinidad, J.F., Carrasco, A.: Finding maximal sequential patterns in text document collections and single documents. Informatica. International Journal of Computing and Informatics (34), 93–101 (2010)Google Scholar
  11. 11.
    Ledeneva, Y., Garcia-Hernandez, R., Gelbukh, A.: Multi-document summarization using Maximal Frequent Sequences. Research in Computer Science 47, 15–24 (2010)Google Scholar
  12. 12.
    Garcia-Hernandez, R., Ledeneva, Y., Gelbukh, A., Citlalih, G.: An Assessment of Word Sequence Models for Extractive Text Summarization. Research in Computing Science (38), 253–262 (2008)Google Scholar
  13. 13.
    Suanmali, L., Salim, N., Salem Binwahlan, M.: Genetic Algorithm based Sentence Extraction for Text Summarization. International Journal of Innovative Computing 1(1) (2011)Google Scholar
  14. 14.
    Silla, C.N., Pappa, G.L., Freitas, A.A., Kaestner, C.A.A.: Automatic text summarization with genetic algorithm-based attribute selection. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 305–314. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Qazvinian, V., Sharif, L., Halavati, R.: Summarising text with a genetic algorithm-based sentence extraction. Int. J. Knowledge Management Studies 2(4), 426–444 (2008)CrossRefGoogle Scholar
  16. 16.
    Cruz, C.M., Urrea, A.M.: Extractive Summarization Based on Word Information and Sentence Position. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 653–656. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Rada, M., Tarau, P.: TextRank: Bringing Order into Texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2004 (2004) Google Scholar
  18. 18.
    van Rijsbergen, C.J., Robertson, S.E., Porter, M.F.: New models in probabilistic information retrieval. En línea (1980) http://tartarus.org/~martin/PorterStemmer/index.html (Último acceso: Enero 28, 2013)
  19. 19.
    Document Understanding Conferences. En línea (Julio 16, 2002), http://www-nlpir.nist.gov/projects/duc/index.html2
  20. 20.
    Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of Workshop on Text Summarization of ACL (2004)Google Scholar
  21. 21.
    Lin, C., Hovy, E.: Automatic Evaluation of Summaries Using N-gram Co-Occurrence. In: Proceedings of HLT-NAACL, Canada, (2003)Google Scholar
  22. 22.
    Ledeneva, Y., Hernández, R.G., Soto, R.M., Reyes, R.C., Gelbukh, A.: EM Clustering Algorithm for Automatic Text Summarization. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 305–315. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • René Arnulfo García-Hernández
    • 1
  • Yulia Ledeneva
    • 1
  1. 1.Autonomous University of the State of MexicoSantiago TianguistencoMéxico

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