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Introduction

  • Parth MehtaEmail author
  • Prasenjit Majumder
Chapter

Abstract

Automatic Summarisation, or reducing a text document while retaining its most essential points, is not a new research area. The first notable attempt, which dates back to 1958, was made by [14]. It uses word frequencies to identify significant words in a given sentence. The importance of a sentence is then determined from the number of significant words it has and proximity of these words to each other. Since then the techniques for both sentence selection (extractive summarisation) as well as abstract generation (abstractive summarisation) have advanced a lot. However there are some aspects of text summarisation which have not received much attention. This book intends to cover those aspects, like domain-specific summarisation and ensemble-based techniques in a greater detail. In this chapter we provide a quick overview of the three major types of summarisation systems along with their pros and cons, a glimpse into the overall content of this book as well as its principle contributions.

References

  1. 1.
    Banko, M., Mittal, V.O., Witbrock, M.J.: Headline generation based on statistical translation. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pp. 318–325. Association for Computational Linguistics (2000)Google Scholar
  2. 2.
    Barzilay, R., McKeown, K.R.: Sentence fusion for multidocument news summarization. Comput. Linguist. 31(3), 297–328 (2005)CrossRefGoogle Scholar
  3. 3.
    Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 484–494 (2016)Google Scholar
  4. 4.
    Chopra, S., Auli, M., Rush, A.M.: Abstractive sentence summarization with attentive recurrent neural networks. In: Proceedings of Human Language Technologies: the 2016 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics (2016)Google Scholar
  5. 5.
    Cohn, T.A., Lapata, M.: Sentence compression as tree transduction. J. Artif. Intell. Res. 34, 637–674 (2009)CrossRefGoogle Scholar
  6. 6.
    Das, D., Martins, A.F.: A survey on automatic text summarization. Lit. Surv. Lang. Stat. II Course CMU 4, 192–195 (2007)Google Scholar
  7. 7.
    Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 457–479, (2004)Google Scholar
  8. 8.
    Filippova, K., Alfonseca, E., Colmenares, C., Kaiser, L., Vinyals, O.: Sentence compression by deletion with lstms. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal (2015)Google Scholar
  9. 9.
    Genest, P.E., Lapalme, G.: Fully abstractive approach to guided summarization. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 354–358. Association for Computational Linguistics (2012)Google Scholar
  10. 10.
    Haghighi, A., Vanderwende, L.: Exploring content models for multi-document summarization. In: Proceedings of Human Language Technologies: the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 362–370. Association for Computational Linguistics (2009)Google Scholar
  11. 11.
    Hong, K., Conroy, J.M., Favre, B., Kulesza, A., Lin, H., Nenkova, A.: A repository of state of the art and competitive baseline summaries for generic news summarization. In: Proceedings of Language Resources and Evaluation Conference, pp. 1608–1616 (2014)Google Scholar
  12. 12.
    Knight, K., Marcu, D.: Summarization beyond sentence extraction: a probabilistic approach to sentence compression. Artif. Intell. 139(1), 91–107 (2002)CrossRefGoogle Scholar
  13. 13.
    Lin, C.Y., Hovy, E.: The automated acquisition of topic signatures for text summarization. In: Proceedings of the 18th conference on Computational linguistics, vol. 1, pp. 495–501. Association for Computational Linguistics (2000)Google Scholar
  14. 14.
    Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. In: Proceedings of Emperical Methods in Natural Language Processing, pp. 404–411. Association for Computational Linguistics, Barcelona, Spain (2004)Google Scholar
  16. 16.
    Moawad, I.F., Aref, M.: Semantic graph reduction approach for abstractive text summarization. In: 2012 Seventh International Conference on Computer Engineering & Systems (ICCES), pp. 132–138. IEEE (2012)Google Scholar
  17. 17.
    Oya, T., Mehdad, Y., Carenini, G., Ng, R.: A template-based abstractive meeting summarization: leveraging summary and source text relationships. In: Proceedings of the 8th International Natural Language Generation Conference (INLG), pp. 45–53 (2014)Google Scholar
  18. 18.
    Radev, D.R., Jing, H., Styś, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Proces. Manag. 40(6), 919–938 (2004)CrossRefGoogle Scholar
  19. 19.
    Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal (2015)Google Scholar
  20. 20.
    Woodsend, K., Feng, Y., Lapata, M.: Generation with quasi-synchronous grammar. In: Proceedings of the 2010 conference on empirical methods in natural language processing, pp. 513–523. Association for Computational Linguistics (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Information Retrieval and Language Processing LabDhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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