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Discursive Sentence Compression

  • Alejandro Molina
  • Juan-Manuel Torres-Moreno
  • Eric SanJuan
  • Iria da Cunha
  • Gerardo Eugenio Sierra Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)

Abstract

This paper presents a method for automatic summarization by deleting intra-sentence discourse segments. First, each sentence is divided into elementary discourse units and, then, less informative segments are deleted. To analyze the results, we have set up an annotation campaign, thanks to which we have found interesting aspects regarding the elimination of discourse segments as an alternative to sentence compression task. Results show that the degree of disagreement in determining the optimal compressed sentence is high and increases with the complexity of the sentence. However, there is some agreement on the decision to delete discourse segments. The informativeness of each segment is calculated using textual energy, a method that has shown good results in automatic summarization.

Keywords

American National Standard Institute Original Sentence Discourse Marker Automatic Summarization Textual Energy 
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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References

  1. 1.
    Edmundson, H.P.: New Methods in Automatic Extraction. Journal of the Association for Computing Machinery 16, 264–285 (1969)MATHCrossRefGoogle Scholar
  2. 2.
    American National Standards Institute Inc.: American National Standard for Writing Abstracts. Technical Report ANSI Z39.14 – 1979, American National Standards Institute, New York (1979)Google Scholar
  3. 3.
    Witbrock, M.J., Mittal, V.O.: Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries. In: Proceedings of the 22nd Conference SIGIR 1999, Berkeley, CA, Etats-Unis, pp. 315–316. ACM (1999)Google Scholar
  4. 4.
    Knight, K., Marcu, D.: Statistics-based summarization – step one: Sentence compression. In: Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, Austin, TX, Etats-Unis, pp. 703–710 (2000)Google Scholar
  5. 5.
    Lin, C.Y.: Improving summarization performance by sentence compression-a pilot study. In: Proceedings of the 6th International Workshop on Information Retrieval with Asian Languages, Sapporo, Japon, pp. 1–8 (2003)Google Scholar
  6. 6.
    Hori, C., Furui, S.: Speech summarization: an approach through word extraction and a method for evaluation. IEICE Transactions on Information and Systems (Institute of Electronics, Informatics and Communication Engineering) 87, 15–25 (2004)Google Scholar
  7. 7.
    Clarke, J., Lapata, M.: Modelling compression with discourse constraints. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1–11 (2007)Google Scholar
  8. 8.
    Steinberger, J., Jezek, K.: Sentence compression for the lsa-based summarizer. In: Proceedings of the 7th International Conference on Information Systems Implementation and Modelling, pp. 141–148 (2006)Google Scholar
  9. 9.
    Steinberger, J., Tesar, R.: Knowledge-poor multilingual sentence compression. In: 7th Conference on Language Engineering (SOLE 2007), Cairo, Egypt, pp. 369–379 (2007)Google Scholar
  10. 10.
    Sporleder, C., Lapata, M.: Discourse chunking and its application to sentence compression. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 257–264 (2005)Google Scholar
  11. 11.
    Molina, A., Torres-Moreno, J.-M., SanJuan, E., da Cunha, I., Sierra, G., Velázquez-Morales, P.: Discourse segmentation for sentence compression. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS (LNAI), vol. 7094, pp. 316–327. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Soricut, R., Marcu, D.: Sentence level discourse parsing using syntactic and lexical information. In: HLT-NAACL (2003)Google Scholar
  13. 13.
    Tofiloski, M., Brooke, J., Taboada, M.: A syntactic and lexical-based discourse segmenter. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers. ACLShort 2009, Stroudsburg, PA, USA, pp. 77–80. Association for Computational Linguistics (2009)Google Scholar
  14. 14.
    Maziero, E., Pardo, T., Nunes, M.: Identificaç ão automática de segmentos discursivos: o uso do parser palavras. Série de relatórios do núcleo interinstitucional de lingüística computacional, Universidade de Sao Paulo, São Carlos, Brésil (2007)Google Scholar
  15. 15.
    da Cunha, I., SanJuan, E., Torres-Moreno, J.-M., Lloberes, M., Castellón, I.: Discourse segmentation for spanish based on shallow parsing. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds.) MICAI 2010, Part I. LNCS, vol. 6437, pp. 13–23. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Afantenos, S.D., Denis, P., Muller, P., Danlos, L.: Learning recursive segments for discourse parsing. CoRR abs/1003.5372 (2010)Google Scholar
  17. 17.
    Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: A Theory of Text Organization. University of Southern California, Information Sciences Institute, California, Marina del Rey (1987)Google Scholar
  18. 18.
    Atserias, J., Casas, B., Comelles, E., González, M., Padró, L., Padró, M.: FreeLing 1.3: Syntactic and semantic services in an open-source NLP library. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), pp. 48–55 (2006)Google Scholar
  19. 19.
    Torres-Moreno, J.M.: Résumé automatique de documents: une approche statistique. Hermés-Lavoisier, Paris (2011)Google Scholar
  20. 20.
    da Cunha, I., Fernández, S., Velázquez Morales, P., Vivaldi, J., SanJuan, E., Torres-Moreno, J.-M.: A new hybrid summarizer based on vector space model, statistical physics and linguistics. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 872–882. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Sierra, G., Torres-Moreno, J.M., Molina, A.: Regroupement sémantique de définitions en espagnol. In: Proceedings of Evaluation des Méthodes d’extraction de Connaissances Dans les Données (EGC/EvalECD 2010), Hammamet, Tunisie, pp. 41–50 (2010)Google Scholar
  22. 22.
    Fernández, S., SanJuan, E., Torres-Moreno, J.-M.: Textual energy of associative memories: performant applications of enertex algorithm in text summarization and topic segmentation. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 861–871. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  24. 24.
    Chen, S., Goodman, J.: An empirical study of smoothing techniques for language modeling. Computer Speech & Language 13, 359–393 (1999)CrossRefGoogle Scholar
  25. 25.
    Stolcke, A.: Srilm – an extensible language modeling toolkit. In: Intl. Conf. on Spoken Language Processing, Denver, vol. 2, pp. 901–904 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alejandro Molina
    • 1
  • Juan-Manuel Torres-Moreno
    • 1
  • Eric SanJuan
    • 1
  • Iria da Cunha
    • 2
  • Gerardo Eugenio Sierra Martínez
    • 3
  1. 1.LIAUniversité d’AvignonFrance
  2. 2.IULAUniversitat Pompeu FabraSpain
  3. 3.GILInstituto de Ingeniería UNAMSpain

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