Global inference for sentence compression an integer linear programming approach

Journal of Artificial Intelligence Research

Volumn 31, Issue , 2008, Pages 399-429

  Clarke, James ^a   Lapata, Mirella ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; GLOBAL OPTIMIZATION; INFERENCE ENGINES; INTEGER PROGRAMMING; LINEAR PROGRAMMING;

GLOBAL INFERENCE; INTEGER LINEAR PROGRAMMING (ILP); SENTENCE COMPRESSION;

DATA COMPRESSION;

EID: 44449150380     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2433     Document Type: Article

References (44)

1. Aho, A. V., & Ullman, J. D. (1969). Syntax directed translations and the pushdown assembler. Journal of Computer and System Sciences, 3, 37-56.
2. Bangalore, S., Rambow, O., & Whittaker, S. (2000). Evaluation metrics for generation. In Proceedings of the first International Conference on Natural Language Generation, pp. 1-8, Mitzpe Ramon, Israel.
3. Barzilay, R., & Lapata, M. (2006). Aggregation via set partitioning for natural language generation. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pp. 359-366, New York, NY, USA.
4. Bramsen, P., Deshpande, P., Lee, Y. K., & Barzilay, R. (2006). Inducing temporal graphs. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 189-198, Sydney, Australia.
5. Briscoe, E. J., & Carroll, J. (2002). Robust accurate statistical annotation of general text. In Proceedings of the Third International Conference on Language Resources and Evaluation, pp. 1499-1504, Las Palmas, Gran Canaria.
6. Charniak, E. (2000). A maximum-entropy-inspired parser. In Proceedings of the 1st North American Annual Meeting of the Association for Computational Linguistics, pp. 132-139, Seattle, WA, USA.
7. Clarke, J., & Lapata, M. (2006). Models for sentence compression: A comparison across domains, training requirements and evaluation measures. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 377-384, Sydney, Australia.
8. Clarkson, P., & Rosenfeld, R. (1997). Statistical language modeling using the CMU-Cambridge toolkit. In Proceedings of Eurospeech'97, pp. 2707-2710, Rhodes, Greece.
9. Cormen, T. H., Leiserson, C. E., & Rivest, R. L. (1992). Intoduction to Algorithms. The MIT Press.
10. Corston-Oliver, S. (2001). Text Compaction for Display on Very Small Screens. In Proceedings of the Workshop on Automatic Summarization at the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, pp. 89-98, Pittsburgh, PA, USA.
11. Crammer, K., & Singer, Y. (2003). Ultra-conservative online algorithms for multiclass problems. Journal of Machine Learning Research, 3, 951-991.
12. Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ, USA.
13. Denis, P., & Baldridge, J. (2007). Joint determination of anaphoricity and coreference resolution using integer programming. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference, pp. 236-243, Rochester, NY.
14. Dras, M. (1999). Tree Adjoining Grammar and the Reluctant Paraphrasing of Text. Ph.D. thesis, Macquarie University.
15. Galley, M., & McKeown, K. (2007). Lexicalized markov grammars for sentence compression. In In Proceedings of the North American Chapter of the Association for Computational Linguistics, pp. 180-187, Rochester, NY, USA.
16. Gomory, R. E. (1960). Solving linear programming problems in integers. In Bellman, R., & Hall, M. (Eds.), Combinatorial analysis, Proceedings of Symposia in Applied Mathematics, Vol. 10, Providence, RI, USA.
17. Grefenstette, G. (1998). 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, pp. 111-117, Stanford, CA, USA.
18. Hori, C., & Furui, S. (2004). Speech summarization: an approach through word extraction and a method for evaluation. IEICE Transactions on Information and Systems, E87-D(1), 15-25.
19. Jing, H. (2000). Sentence reduction for automatic text summarization. In Proceedings of the 6th Applied Natural Language Processing Conference, pp. 310-315, Seattle,WA, USA.
20. Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: a probabilistic approach to sentence compression. Artificial Intelligence, 139(1), 91-107.
21. Land, A. H., & Doig, A. G. (1960). An automatic method for solving discrete programming problems. Econometrica, 28, 497-520.
22. Lin, C.-Y. (2003). Improving summarization performance by sentence compression -a pilot study. In Proceedings of the 6th International Workshop on Information Retrieval with Asian Languages, pp. 1-8, Sapporo, Japan.
23. Lin, D. (2001). LaTaT: Language and text analysis tools. In Proceedings of the first Human Language Technology Conference, pp. 222-227, San Francisco, CA, USA.
24. Marciniak, T., & Strube, M. (2005). Beyond the pipeline: Discrete optimization in NLP. In Proceedings of the Ninth Conference on Computational Natural Language Learning, pp. 136-143, Ann Arbor, MI, USA.
25. McDonald, R. (2006). 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.
26. McDonald, R., Crammer, K., & Pereira, F. (2005a). Flexible text segmentation with structured multilabel classification. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 987-994, Vancouver, BC, Canada.
27. McDonald, R., Crammer, K., & Pereira, F. (2005b). Online large-margin training of dependency parsers. In 43rd Annual Meeting of the Association for Computational Linguistics, pp. 91-98, Ann Arbor, MI, USA.
28. Nemhauser, G. L., & Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience series in discrete mathematicals and opitmization. Wiley, New York, NY, USA.
29. Nguyen, M. L., Shimazu, A., Horiguchi, S., Ho, T. B., & Fukushi, M. (2004). Probabilistic sentence reduction using support vector machines. In Proceedings of the 20th international conference on Computational Linguistics, pp. 743-749, Geneva, Switzerland.
30. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, New York, NY, USA.
31. Punyakanok, V., Roth, D., Yih, W., & Zimak, D. (2004). Semantic role labeling via integer linear programming inference. In Proceedings of the International Conference on Computational Linguistics, pp. 1346-1352, Geneva, Switzerland.
32. Riedel, S., & Clarke, J. (2006). Incremental integer linear programming for non-projective dependency parsing. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 129-137, Sydney, Australia.
33. Riezler, S., King, T. H., Crouch, R., & Zaenen, A. (2003). Statistical sentence condensation using ambiguity packing and stochastic disambiguation methods for lexical-functional grammar. In Human Language Technology Conference and the 3rd Meeting of the North American Chapter of the Association for Computational Linguistics, pp. 118-125, Edmonton, Canada.
34. Roark, B. (2001). Probabilistic top-down parsing and language modeling. Computational Linguistics, 27(2), 249-276.
35. Roth, D. (1998). Learning to resolve natural language ambiguities: A unified approach. In In Proceedings of the 15th of the American Association for Artificial Intelligence, pp. 806-813, Madison, WI, USA.
36. Roth, D., & Yih, W. (2004). A linear programming formulation for global inference in natural language tasks. In Proceedings of the Annual Conference on Computational Natural Language Learning, pp. 1-8, Boston, MA, USA.
37. Roth, D., & Yih, W. (2005). Integer linear programming inference for conditional random fields. In Proceedings of the International Conference on Machine Learning, pp. 737-744, Bonn.
38. Sarawagi, S., & Cohen, W. W. (2004). Semi-markov conditional random fields for information extraction. In Advances in Neural Information Processing Systems, Vancouver, BC, Canada.
39. Shieber, S., & Schabes, Y. (1990). Synchronous tree-adjoining grammars. In Proceedings of the 13th International Conference on Computational Linguistics, pp. 253-258, Helsinki, Finland.
40. Turner, J., & Charniak, E. (2005). Supervised and unsupervised learning for sentence compression. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp. 290-297, Ann Arbor, MI, USA.
41. Vandeghinste, V., & Pan, Y. (2004). Sentence compression for automated subtitling: A hybrid approach. In Marie-Francine Moens, S. S. (Ed.), Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pp. 89-95, Barcelona, Spain.
42. Williams, H. P. (1999). Model Building in Mathematical Programming (4th edition). Wiley.
43. Winston, W. L., & Venkataramanan, M. (2003). Introduction to Mathematical Programming: Applications and Algorithms (4th edition). Duxbury.
44. Zajic, D., Door, B. J., Lin, J., & Schwartz, R. (2007). Multi-candidate reduction: Sentence compression as a tool for document summarization tasks. Information Processing Management Special Issue on Summarization, 43(6), 1549-1570.