Advertisement

A Comprehensive Survey on Extractive and Abstractive Techniques for Text Summarization

  • Abhishek MahajaniEmail author
  • Vinay Pandya
  • Isaac Maria
  • Deepak Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)

Abstract

Over the years as the technology advanced, the amount of data generated during the simulations and processing has been constantly increasing. Techniques for creating synopses of this massively generated data have been in the forefront of the research in the recent times. Text Summarization was one such aspect of the research which focused on representing the idea of the context in a short representation. Efforts were put to create a system which was able to generate effective summaries providing an overview of all the ideas represented by the article. Text Summarization techniques can be broadly classified into Extractive and Abstractive Text Summarization techniques. The paper compares all the prevailing systems, their shortcomings, and a combination of technologies used to achieve improved results. The paper also draws attention towards the state-of-the-art standardized datasets used in developing the summarization systems. The paper also focuses on testing parameters and techniques used to test the efficiency of the summarizing systems.

Keywords

Extractive text summarization Abstractive text summarization 

References

  1. 1.
    Moratanch, N., Chitrakala, S.: A survey on abstractive text summarization. In: 2016 International Conference on Circuit, power and computing technologies (ICCPCT) (pp. 1–7). IEEE (2016)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.
    Lee, C.S., Jian, Z.W., Huang, L.K.: A fuzzy ontology and its application to news summarization. IEEE Trans. Syst. Man, Cybern. Part B (Cybern.) 35(5), 859–880 (2005)Google Scholar
  4. 4.
    John, A., Wilscy, M.: Random forest classifier based multi-document summarization system. In: 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 31–36. IEEE (2013)Google Scholar
  5. 5.
    Ganesan, K., Zhai, C., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 340–348. Association for Computational Linguistics (2010)Google Scholar
  6. 6.
    Lloret, E., Palomar, M.: Analyzing the use of word graphs for abstractive text summarization. In: Proceedings of the First International Conference on Advances in Information Mining and Management, pp. 61–6. Barcelona, Spain (2011)Google Scholar
  7. 7.
    Gupta, V., Lehal, G.S.: A survey of text summarization extractive techniques. J. Emerg. Technol. web Intell. 2(3), 258–268 (2010)Google Scholar
  8. 8.
    Zhang, J., Sun, L., Zhou, Q.: A cue-based hub-authority approach for multidocument text summarization. In: Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE’05, pp. 642–645. IEEE (2005)Google Scholar
  9. 9.
    Ferreira, R., Freitas, F., de Souza Cabral, L., Lins, R.D., Lima, R., França, G…., Favaro, L.: A context based text summarization system. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 66–70. IEEE (2014)Google Scholar
  10. 10.
    Zhang, P.Y., & Li, C.H.: Automatic text summarization based on sentences clustering and extraction. In: 2nd IEEE International Conference on Computer Science and Information Technology, 2009. ICCSIT 2009, pp. 167–170. IEEE (2009)Google Scholar
  11. 11.
    Nallapati, R., Zhou, B., Ma, M.: Classify or select: neural architectures for extractive document summarization. arXiv preprint arXiv:1611.04244 (2016)
  12. 12.
    Narayan, S., Papasarantopoulos, N., Lapata, M., Cohen, S.B.: Neural extractive summarization with side information. arXiv preprint arXiv:1704.04530 (2017)
  13. 13.
    Narayan, S., Papasarantopoulos, N., Cohen, S.B., Lapata, M.: Neural extractive summarization with side information. arXiv preprint arXiv:1704.04530 (2017)
  14. 14.
    Erkan, G., Radev, D.R.: Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22:457–479 (2004)Google Scholar
  15. 15.
    Steinberger, J., Ježek, K.: Text summarization and singular value decomposition. In: International Conference on Advances in Information Systems, pp. 245–254. Springer, Berlin, Heidelberg (2004)Google Scholar
  16. 16.
    Peddinti, V., Povey, D., Khudanpur, S.: A time delay neural network architecture for efficient modeling of long temporal contexts. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)Google Scholar
  17. 17.
    Li, J., Luong, M.T., Jurafsky, D.: A hierarchical neural autoencoder for paragraphs and documents. arXiv preprint arXiv:1506.01057 (2015)
  18. 18.
    Dohare, S., Karnick, H.: Text summarization using abstract meaning representation. arXiv preprint arXiv:1706.01678 (2017)
  19. 19.
    Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. arXiv preprint arXiv:1603.07252 (2016)
  20. 20.
    Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B.: Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023 (2016)
  21. 21.
    Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhishek Mahajani
    • 1
    Email author
  • Vinay Pandya
    • 1
  • Isaac Maria
    • 1
  • Deepak Sharma
    • 1
  1. 1.Department of Computer EngineeringKJSCEMumbaiIndia

Personalised recommendations