Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Machine Learning
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Sentence compression beyond word deletion
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Sentence compression as tree transduction
Journal of Artificial Intelligence Research
An extractive supervised two-stage method for sentence compression
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Semantic role features for machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Discourse constraints for document compression
Computational Linguistics
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For sentence compression, we propose new semantic constraints to directly capture the relations between a predicate and its arguments, whereas the existing approaches have focused on relatively shallow linguistic properties, such as lexical and syntactic information. These constraints are based on semantic roles and superior to the constraints of syntactic dependencies. Our empirical evaluation on the Written News Compression Corpus (Clarke and Lapata, 2008) demonstrates that our system achieves results comparable to other state-of-the-art techniques.