A maximum entropy approach to natural language processing
Computational Linguistics
Summarization beyond sentence extraction: a probabilistic approach to sentence compression
Artificial Intelligence
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Example-based sentence reduction using the hidden markov model
ACM Transactions on Asian Language Information Processing (TALIP)
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Supervised and unsupervised learning for sentence compression
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Trimming CFG parse trees for sentence compression using machine learning approaches
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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Sentence compression is a task of generating a grammatical short sentence from an original sentence, retaining the most important information. The existing methods of removing the constituents in the parse tree of an original sentence cannot deal with recursive structures which appear in the parse tree. This paper proposes a method to remove such structure and generate a grammatical short sentence. Compression experiments have shown the method to provide an ability to sentence compression comparable to the existing methods and generate good compressed sentences for sentences including recursive structures, which the previous methods failed to compress.