Abstract
With the tremendous increasing of information, the demands of information from people advanced the development of Nature Language Processing (NLP). As a consequent, Sentence compression, which is an important part of automatic summarization, draws much more attention. Sentence compression has been widely used in automatic title generation, Searching Engine, Topic detection and Summarization. Under the framework of discriminative model, this paper presents a decoding method based on Integer Linear Programming (ILP), which considers sentence compression as the selection of the optimal compressed target sentence. Experiment results show that the ILP-based system maintains a good compression ratio while remaining the main information of source sentence. Compared to other decoding method, this method has the advantage of speed and using fewer features in the case of similar results obtained.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Corston-Oliver, S.: Text Compaction for Display on Very Small Screens. In: Proceedings of the NAACL Workshop on Automatic Summarization, Pittsburgh, PA, pp. 89–98 (2001)
Clarke, J., Lapata, M.: Global Inference for Sentence Compression An Integer Linear Programming Approach. Journal of Artificial Intelligence 31, 399–429 (2008)
Vandeghinste, V., Pan, Y.: Sentence compression for automated subtitling: A hybrid approach. In: Marie-Francine Moens, S.S. (ed.) Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, Barcelona, Spain, pp. 89–95 (2004)
Grefenstette, G.: Producing Intelligent Telegraphic Text Reduction to Provide an Audio Scanning Service for the Blind. In: Hovy, E., Radev, D.R. (eds.) Proceedings of the AAAI Symposium on Intelligent Text Summarization, Stanford, CA, USA, pp. 111–117 (1998)
Knight, K., Marcu, D.: Summarization beyond sentence extraction: a probabilistic approach to sentence compression. Artificial Intelligence 139(1), 91–107 (2002)
McDonald, R., Crammer, K., Pereira, F.: Online large-margin training of dependency parsers. In: 43rd Annual Meeting of the Association for Computational Linguistics, Ann Arbor, MI, USA, pp. 91–98 (2005b)
Sang, E.F.T.K., Meulder, F.: Introduction to the conll-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Learning at HLT-NAACL 2003, pp. 142–147. Association for Computational Linguistics, Morristown (2003)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)
McDonald, R.: Discriminative sentence compression with soft syntactic constraints. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy, pp. 297–309 (2006)
Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 6, 1453–1484 (2005)
Cohn, T., Lapata, M.: Large margin synchronous generation and its application to sentence compression. In: Proceedings of the EMNLP/CoNLL 2007, Prague, Czech Republic, pp. 73–82 (2007)
Cohn, T., Lapata, M.: Sentence compression beyond word deletion. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, pp. 137–144 (2008)
Cohn, T., Lapata, M.: Sentence Compression as Tree Transduction. Journal of Artificial Intelligence Research 34, 637–674 (2009)
Roth, D., Yih, W.: A linear programming formulation for global inference in natural language tasks. In: Proceedings of the Annual Conference on Computational Natural Language Learning, Boston, MA, USA, pp. 1–8 (2004)
Punyakanok, V., Roth, D., Yih, W., Zimak, D.: Semantic role labeling via integer linear programming inference. In: Proceedings of the International Conference on Computational Linguistics, Geneva, Switzerland, pp. 1346–1352 (2004)
Riedel, S., Clarke, J.: Incremental integer linear programming for non-projective dependency parsing. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia, pp. 129–137 (2006)
Gillick, D., Favre, B.: A scalable global model for summarization. In: Proc. of ACL Workshop on Integer Linear Programming for Natural Language Processing, Boulder, Colorado, pp. 10–18 (2009)
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, Portland, Oregon, pp. 481–490 (2011)
Woodsend, K., Lapata, M.: Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming. In: EMNLP 2011, pp. 409–420 (2011)
Zhang, Y.L., Wang, H.L., Zhou, G.D.: Sentence Compression Based on Structured Learning. Journal of Chinese Information Processing 27(2), 10–16 (2013)
Zhang, Y., Peng, C., Wang, H.: Research on Chinese Sentence Compression for the Title Generation. In: Ji, D., Xiao, G. (eds.) CLSW 2012. LNCS, vol. 7717, pp. 22–31. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Zhang, Y., Zhou, G. (2013). Sentence Compression Based on ILP Decoding Method. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-41644-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41643-9
Online ISBN: 978-3-642-41644-6
eBook Packages: Computer ScienceComputer Science (R0)