Abstract
Deletion-based sentence compression is frequently formulated as a constrained optimization problem and solved by integer linear programming (ILP). However, ILP methods searching the best compression given the space of all possible compressions would be intractable when dealing with overly long sentences and too many constraints. Moreover, the hard constraints of ILP would restrict the available solutions. This problem could be even more severe considering parsing errors. As an alternative solution, we formulate this task in a reinforcement learning framework, where hard constraints are used as rewards in a soft manner. The experiment results show that our method achieves competitive performance with a large improvement on the speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
BNCNews and BroadCast are available at http://jamesclarke.net/research/resources/.
- 3.
Accessible from www.keithv.com/software/csr/.
- 4.
Methods with syntactic features have a similar pattern.
References
Clarke, J., Lapata, M.: Global inference for sentence compression: an integer linear programming approach. J. Artif. Intell. Res. 31, 399–429 (2008)
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 (2015)
Filippova, K., Strube, M.: Dependency tree based sentence compression. In: Proceedings of the Fifth International Natural Language Generation Conference (2008)
Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 273–278. IEEE (2013)
Grissom II, A.: Don’t until the final verb wait: reinforcement learning for simultaneous machine translation (2014)
Hori, C., Furui, S.: Speech summarization: an approach through word extraction and a method for evaluation. IEICE Trans. Inf. Syst. 87(1), 15–25 (2004)
Jing, H.: Sentence reduction for automatic text summarization. In: Proceedings of the Sixth Conference on Applied Natural Language Processing (2000)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (2015)
Knight, K., Marcu, D.: Statistics-based summarization-step one: sentence compression. In: Proceedings of the 17th National Conference on Artificial Intelligence (2000)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL-04 workshop, vol. 8. Barcelona, Spain (2004)
McDonald, R.T.: Discriminative sentence compression with soft syntactic evidence. In: Proceedings of European Chapter of the Association for Computational Linguistics Valencia (2006)
Mnih, V., et al.: Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
Narasimhan, K., Yala, A., Barzilay, R.: Improving information extraction by acquiring external evidence with reinforcement learning. arXiv preprint arXiv:1603.07954 (2016)
Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2014)
Ryang, S., Abekawa, T.: Framework of automatic text summarization using reinforcement learning. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 256–265. Association for Computational Linguistics (2012)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, vol. 1. MIT press, Cambridge (1998)
Turner, J., Charniak, E.: Supervised and unsupervised learning for sentence compression. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 290–297. Association for Computational Linguistics (2005)
Vandeghinste, V., Pan, Y.: Sentence compression for automated subtitling: a hybrid approach. In: Proceedings of the ACL workshop on Text Summarization, pp. 89–95 (2004)
Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Advances in Neural Information Processing Systems, pp. 2692–2700 (2015)
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: Meeting of the Association for Computational Linguistics, pp. 1385–1393 (2017)
Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, L., Jiang, J., Liao, L. (2018). Sentence Compression with Reinforcement Learning. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-99365-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99364-5
Online ISBN: 978-3-319-99365-2
eBook Packages: Computer ScienceComputer Science (R0)