Skip to main content
SpringerLink
Log in

Related Work

  • Chapter
  • First Online:
From Extractive to Abstractive Summarization: A Journey

  • 388 Accesses

Abstract

In this chapter, we examine some of the existing techniques for sentence extraction and sentence compression. We also discuss the existing approaches for creating ensembles of these systems. We point out specific cases where the existing techniques would not work and what needs to be done to handle such cases. We discuss various existing approaches for extractive summarisation in such a way as to maximise the representation from several different categories of extractive techniques. In contrast to the extractive techniques, ensemble techniques for summarisation are fewer, but at the same time warrant a detailed discussion in order to present our arguments. We discuss all the existing ensemble approaches, their strengths and weaknesses. For abstractive summarisation/sentence compression we classify the approaches into two major groups: those dependent on linguistic resources and the ones that are completely data-driven. We end the chapter with an overview of domain-specific summarisation approaches, specifically related to legal and scientific articles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://tac.nist.gov//2014/BiomedSumm/.

  2. 2.

    https://www.nlm.nih.gov/research/umls/.

References

  1. Abu-Jbara, A., Radev, D.: Coherent citation-based summarization of scientific papers. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 500–509. Association for Computational Linguistics (2011)

    Google Scholar 

  2. 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 

  3. Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.M., Nunes, C., Jatowt, A.: A text feature based automatic keyword extraction method for single documents. In: European Conference on Information Retrieval, pp. 684–691. Springer (2018)

    Google Scholar 

  4. 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 

  5. 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 

  6. Clarke, J., Lapata, M.: Global inference for sentence compression: an integer linear programming approach. J. Artif. Intell. Res. 31, 399–429 (2008)

    Article  Google Scholar 

  7. Cohn, T., Lapata, M.: Sentence compression beyond word deletion. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 137–144. Association for Computational Linguistics (2008)

    Google Scholar 

  8. Cohn, T.A., Lapata, M.: Sentence compression as tree transduction. J. Artif Intell. Res. 34, 637–674 (2009)

    Article  Google Scholar 

  9. Conroy, J.M., Schlesinger, J.D., O’Leary, D.P.: Topic-focused multi-document summarization using an approximate oracle score. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 152–159. Association for Computational Linguistics (2006)

    Google Scholar 

  10. Daumé III, H., Marcu, D.: A noisy-channel model for document compression. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 449–456. Association for Computational Linguistics (2002)

    Google Scholar 

  11. Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)

    Article  Google Scholar 

  12. Filippova, K., Alfonseca, E., Colmenares, C.A., Kaiser, L., Vinyals, O.: Sentence compression by deletion with lstms. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 360–368. Association for Computational Linguistics, Lisbon, Portugal (2015)

    Google Scholar 

  13. Filippova, K., Altun, Y.: Overcoming the lack of parallel data in sentence compression. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1481–1491 (2013)

    Google Scholar 

  14. Fiszman, M., Rindflesch, T.C., Kilicoglu, H.: Abstraction summarization for managing the biomedical research literature. In: Proceedings of the HLT-NAACL workshop on computational lexical semantics, pp. 76–83. Association for Computational Linguistics (2004)

    Google Scholar 

  15. Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domain-specific keyphrase extraction. In: 16th International Joint Conference on Artificial Intelligence (IJCAI 99), vol. 2, pp. 668–673. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)

    Google Scholar 

  16. Galgani, F., Compton, P., Hoffmann, A.: Hauss: Incrementally building a summarizer combining multiple techniques. Int. J. Hum-Comput. Stud. 72(7), 584–605 (2014)

    Article  Google Scholar 

  17. Grover, C., Hachey, B., Korycinski, C.: Summarising legal texts: sentential tense and argumentative roles. In: Proceedings of the HLT-NAACL 03 on Text summarization workshop, vol. 5, pp. 33–40. Association for Computational Linguistics (2003)

    Google Scholar 

  18. Gu, J., Lu, Z., Li, H., Li, V.O.: Incorporating copying mechanism in sequence-to-sequence learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1631–1640 (2016)

    Google Scholar 

  19. Gulcehre, C., Ahn, S., Nallapati, R., Zhou, B., Bengio, Y.: Pointing the unknown words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 140–149 (2016)

    Google Scholar 

  20. 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 

  21. Hirohata, K., Okazaki, N., Ananiadou, S., Ishizuka, M.: Identifying sections in scientific abstracts using conditional random fields. In: Proceedings of the Third International Joint Conference on Natural Language Processing, vol. I (2008)

    Google Scholar 

  22. Hong, K., Marcus, M., Nenkova, A.: System combination for multi-document summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 107–117. Association for Computational Linguistics, Lisbon, Portugal (2015)

    Google Scholar 

  23. Jaidka, K., Chandrasekaran, M.K., Elizalde, B.F., Jha, R., Jones, C., Kan, M.Y., Khanna, A., Molla-Aliod, D., Radev, D.R., Ronzano, F., et al.: The computational linguistics summarization pilot task. In: Proceedings of the Text Analysis Conference (TAC 2011), Gaithersburg, Maryland, USA (2014)

    Google Scholar 

  24. Jaidka, K., Chandrasekaran, M.K., Rustagi, S., Kan, M.Y.: Overview of the cl-scisumm 2016 shared task. In: Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL), pp. 93–102 (2016)

    Google Scholar 

  25. Kågebäck, M., Mogren, O., Tahmasebi, N., Dubhashi, D.: Extractive summarization using continuous vector space models. In: Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)@ EACL, pp. 31–39. Citeseer (2014)

    Google Scholar 

  26. Knight, K., Marcu, D.: Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artif. Intell. 139(1), 91–107 (2002)

    Article  Google Scholar 

  27. Kobayashi, H., Noguchi, M., Yatsuka, T.: Summarization based on embedding distributions. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1984–1989. Association for Computational Linguistics, Lisbon, Portugal (2015)

    Google Scholar 

  28. Kulesza, A., Taskar, B., et al.: Determinantal point processes for machine learning. Found. Trends Mach. Learn. 5(2–3), 123–286 (2012)

    Article  Google Scholar 

  29. 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 

  30. Lin, H., Bilmes, J.: A class of submodular functions for document summarization. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 510–520. Association for Computational Linguistics (2011)

    Google Scholar 

  31. Lin, H., Bilmes, J.: Learning mixtures of submodular shells with application to document summarization. In: Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, pp. 479–490. AUAI Press (2012)

    Google Scholar 

  32. Mandal, A., Ghosh, K., Pal, A., Ghosh, S.: Automatic catchphrase identification from legal court case documents. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2187–2190. ACM (2017)

    Google Scholar 

  33. Mehta, P.: From extractive to abstractive summarization: A journey. In: Proceedings of the ACL 2016 Student Research Workshop, Germany, pp. 100–106. ACL (2016). https://doi.org/10.18653/v1/P16-3015

  34. Mehta, P., Arora, G., Majumder, P.: Attention Based Sentence Extraction from Scientific Articles Using Pseudo-Labeled Data (2018). arXiv:1802.04675

  35. Mehta, P., Majumder, P.: Content based weighted consensus summarization. In: European Conference on Information Retrieval, pp. 787–793. Springer (2018)

    Google Scholar 

  36. Mehta, P., Majumder, P.: Effective aggregation of various summarization techniques. Inf. Proces. Manag. 54(2), 145–158 (2018)

    Article  Google Scholar 

  37. Mehta, P., Majumder, P.: Exploiting Local and Global Performance of Candidate Systems for Aggregation of Summarization Techniques (2018). arXiv:1809.02343

  38. Mei, Q., Zhai, C.: Generating impact-based summaries for scientific literature. In: Proceedings of ACL-08: HLT, pp. 816–824 (2008)

    Google Scholar 

  39. 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 

  40. Moens, M.F., Uyttendaele, C.: Automatic text structuring and categorization as a first step in summarizing legal cases. Inf. Proces. Manag. 33(6), 727–737 (1997)

    Article  Google Scholar 

  41. Mogren, O., Kågebäck, M., Dubhashi, D.: Extractive summarization by aggregating multiple similarities. In: Proceedings of Recent Advances In Natural Language Processing, pp. 451–457 (2015)

    Google Scholar 

  42. Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Mining Text Data, pp. 43–76. Springer (2012)

    Google Scholar 

  43. Nenkova, A., Vanderwende, L., McKeown, K.: A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 573–580. ACM (2006)

    Google Scholar 

  44. 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 

  45. Page, L., Brin, S., Motwani, R., Winograd, T.: The Pagerank Citation Ranking: Bringing Order to the Web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  46. Pei, Y., Yin, W., Fan, Q., Huang, L.: A supervised aggregation framework for multi-document summarization. In: Proceedings of 24th International Conference on Computational Linguistics: Technical Papers, pp. 2225–2242 (2012)

    Google Scholar 

  47. Qazvinian, V., Radev, D.R.: Scientific paper summarization using citation summary networks. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 689–696. Association for Computational Linguistics (2008)

    Google Scholar 

  48. Qazvinian, V., Radev, D.R., Mohammad, S.M., Dorr, B., Zajic, D., Whidby, M., Moon, T.: Generating extractive summaries of scientific paradigms. J. Artif. Intell. Res. 46, 165–201 (2013)

    Article  MathSciNet  Google Scholar 

  49. Radev, D.R., Jing, H., Styś, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Process. Manag. 40(6), 919–938 (2004)

    Article  Google Scholar 

  50. Reeve, L.H., Han, H., Brooks, A.D.: The use of domain-specific concepts in biomedical text summarization. Inf. Process. Manag. 43(6), 1765–1776 (2007)

    Article  Google Scholar 

  51. 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 

  52. Salton, G., Singhal, A., Mitra, M., Buckley, C.: Automatic text structuring and summarization. Inf. Process. Manag. 33(2), 193–207 (1997)

    Article  Google Scholar 

  53. SanJuan, E., Ibekwe-SanJuan, F., Torres-Moreno, J.M., Velázquez-Morales, P.: Combining vector space model and multi word term extraction for semantic query expansion. Natural Language Processing and Information Systems pp. 252–263 (2007)

    Google Scholar 

  54. Saravanan, M., Ravindran, B.: Identification of rhetorical roles for segmentation and summarization of a legal judgment. Artif. Intell. Law 18(1), 45–76 (2010)

    Article  Google Scholar 

  55. Saravanan, M., Ravindran, B., Raman, S.: Improving legal document summarization using graphical models. Front. Artif. Intell. Appl. 152, 51 (2006)

    Google Scholar 

  56. Saravanan, M., Ravindran, B., Raman, S.: Using legal ontology for query enhancement in generating a document summary. Front. Artif. Intell. Appl. 165, 171 (2007)

    Google Scholar 

  57. Saravanan, M., Ravindran, B., Raman, S.: Automatic identification of rhetorical roles using conditional random fields for legal document summarization. In: Proceedings of the Third International Joint Conference on Natural Language Processing, vol. I (2008)

    Google Scholar 

  58. Steinberger, J.: Using latent semantic analysis in text summarization and summary evaluation. In: Proceedings of ISIM04, pp. 93–100 (2004)

    Google Scholar 

  59. Suanmali, L., Binwahlan, M.S., Salim, N.: Sentence features fusion for text summarization using fuzzy logic. In: Ninth International Conference on Hybrid Intelligent Systems, 2009. HIS’09, vol. 1, pp. 142–146. IEEE (2009)

    Google Scholar 

  60. Teufel, S., Moens, M.: Summarizing scientific articles: experiments with relevance and rhetorical status. Comput. Linguist. 28(4), 409–445 (2002)

    Article  Google Scholar 

  61. Wang, D., Li, T.: Weighted consensus multi-document summarization. Inf. Process. Manag. 48(3), 513–523 (2012)

    Article  Google Scholar 

  62. 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 

  63. Woodsend, K., Lapata, M.: Automatic generation of story highlights. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 565–574. Association for Computational Linguistics (2010)

    Google Scholar 

Download references

Acknowledgements

Adapted/Translated by permission from Elsevier: Elsevier, Information processing and management, Vol 54/2, pages no. 145–158, Effective aggregation of various summarization techniques, Parth Mehta and Prasenjit Majumder, Copyright (2018).

Author information

Authors and Affiliations

  1. Information Retrieval and Language Processing Lab, Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India

    Parth Mehta & Prasenjit Majumder

Authors
  1. Parth Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prasenjit Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parth Mehta .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mehta, P., Majumder, P. (2019). Related Work. In: From Extractive to Abstractive Summarization: A Journey. Springer, Singapore. https://doi.org/10.1007/978-981-13-8934-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-8934-4_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8933-7

  • Online ISBN: 978-981-13-8934-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation