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Domain Adaptive Neural Sentence Compression by Tree Cutting

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

Sentence compression has traditionally been tackled as syntactic tree pruning, where rules and statistical features are defined for pruning less relevant words. Recent years have witnessed the rise of neural models without leveraging syntax trees, learning sentence representations automatically and pruning words from such representations. We investigate syntax tree based noise pruning methods for neural sentence compression. Our method identifies the most informative regions in a syntactic dependency tree by self attention over context nodes and maximum density subtree extraction. Empirical results show that the model outperforms the state-of-the-art methods in terms of both accuracy and F1-measure. The model also yields a comparable accuracy in readability and informativeness as assessed by human evaluators.

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Notes

  1. 1.

    http://storage.googleapis.com/sentencecomp/compression-data.json.

  2. 2.

    http://jamesclarke.net/research/resources/.

  3. 3.

    https://nlp.stanford.edu/software/lex-parser.shtml.

  4. 4.

    http://opennmt.net/.

References

  1. Berg-Kirkpatrick, T., Gillick, D., Klein, D.: Jointly learning to extract and compress. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 481–490. Association for Computational Linguistics (2011)

    Google Scholar 

  2. Chopra, S., Auli, M., Rush, A.M.: Abstractive sentence summarization with attentive recurrent neural networks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–98 (2016)

    Google Scholar 

  3. 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 

  4. Cohn, T., Lapata, M.: Large margin synchronous generation and its application to sentence compression. In: EMNLP-CoNLL, pp. 73–82 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  6. Filippova, K., Alfonseca, E., Colmenares, C.A., Kaiser, L., Vinyals, O.: Sentence compression by deletion with LSTMs. In: EMNLP, pp. 360–368 (2015)

    Google Scholar 

  7. Filippova, K., Strube, M.: Dependency tree based sentence compression. In: Proceedings of the Fifth International Natural Language Generation Conference, pp. 25–32. Association for Computational Linguistics (2008)

    Google Scholar 

  8. Galanis, D., Androutsopoulos, I.: An extractive supervised two-stage method for sentence compression. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 885–893. Association for Computational Linguistics (2010)

    Google Scholar 

  9. Graves, A., Jaitly, N., Mohamed, A.r.: Hybrid speech recognition with deep bidirectional LSTM. In: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 273–278. IEEE (2013)

    Google Scholar 

  10. Hochreiter, S., Schmidhuber, J.: LSTM can solve hard long time lag problems. In: Advances in Neural Information Processing Systems, pp. 473–479 (1997)

    Google Scholar 

  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: LREC, pp. 1608–1616 (2014)

    Google Scholar 

  12. Huang, M., Shi, X., Jin, F., Zhu, X.: Using first-order logic to compress sentences. In: AAAI (2012)

    Google Scholar 

  13. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  14. McDonald, R.T.: Discriminative sentence compression with soft syntactic evidence. In: EACL (2006)

    Google Scholar 

  15. Napoles, C., Gormley, M., Van Durme, B.: Annotated gigaword. In: Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, pp. 95–100. Association for Computational Linguistics (2012)

    Google Scholar 

  16. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  17. Qian, X., Liu, Y.: Polynomial time joint structural inference for sentence compression. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 327–332 (2014)

    Google Scholar 

  18. Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015)

  19. Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks (2015)

    Google Scholar 

  20. Takase, S., Suzuki, J., Okazaki, N., Hirao, T., Nagata, M.: Neural headline generation on abstract meaning representation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1054–1059 (2016)

    Google Scholar 

  21. Thadani, K.: Approximation strategies for multi-structure sentence compression. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1241–1251 (2014)

    Google Scholar 

  22. Thadani, K., McKeown, K.: Sentence compression with joint structural inference. In: Proceedings of the Seventeenth Conference on Computational Natural Language Learning, pp. 65–74 (2013)

    Google Scholar 

  23. Wang, L., Jiang, J., Chieu, H.L., Ong, C.H., Song, D., Liao, L.: Can syntax help? Improving an LSTM-based sentence compression model for new domains. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1385–1393 (2017)

    Google Scholar 

  24. Yao, J.g., Wan, X.: Greedy flipping for constrained word deletion. In: AAAI, pp. 3518–3524 (2017)

    Google Scholar 

  25. Yoshikawa, K., Hirao, T., Iida, R., Okumura, M.: Sentence compression with semantic role constraints. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pp. 349–353. Association for Computational Linguistics (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. International Institute of Information Technology, Hyderabad, Hyderabad, 500031, India

    Litton J. Kurisinkel & Vasudeva Varma

  2. Westlake Institute for Advanced Study, West Lake University, Hangzhou, Zhejiang Province, China

    Yue Zhang

Authors
  1. Litton J. Kurisinkel
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  2. Yue Zhang
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  3. Vasudeva Varma
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Corresponding author

Correspondence to Litton J. Kurisinkel .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Kurisinkel, L.J., Zhang, Y., Varma, V. (2019). Domain Adaptive Neural Sentence Compression by Tree Cutting. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_31

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  • DOI: https://doi.org/10.1007/978-3-030-15712-8_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15711-1

  • Online ISBN: 978-3-030-15712-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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