Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
Text Revision: A Model and Its Implementation
Proceedings of the 6th International Workshop on Natural Language Generation: Aspects of Automated Natural Language Generation
Discovery of inference rules for question-answering
Natural Language Engineering
Cut and paste based text summarization
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Improving summarization performance by sentence compression: a pilot study
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Extracting structural paraphrases from aligned monolingual corpora
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Supervised and unsupervised learning for sentence compression
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Multi-candidate reduction: Sentence compression as a tool for document summarization tasks
Information Processing and Management: an International Journal
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Paraphrastic sentence compression with a character-based metric: tightening without deletion
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Integer linear programming for dutch sentence compression
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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Data-driven approaches to sentence compression define the task as dropping any subset of words from the input sentence while retaining important information and grammaticality. We show that only 16% of the observed compressed sentences in the domain of subtitling can be accounted for in this way. We argue that part of this is due to evaluation issues and estimate that a deletion model is in fact compatible with approximately 55% of the observed data. We analyse the remaining problems and conclude that in those cases word order changes and paraphrasing are crucial, and argue for more elaborate sentence compression models which build on NLG work.