Supervised and unsupervised learning for sentence compression

Authors:
Jenine Turner;Eugene Charniak
Affiliations:
Brown University, Providence, RI;Brown University, Providence, RI
Venue:
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Year:
2005

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Abstract

In Statistics-Based Summarization - Step One: Sentence Compression, Knight and Marcu (Knight and Marcu, 2000) (K&M) present a noisy-channel model for sentence compression. The main difficulty in using this method is the lack of data; Knight and Marcu use a corpus of 1035 training sentences. More data is not easily available, so in addition to improving the original K&M noisy-channel model, we create unsupervised and semi-supervised models of the task. Finally, we point out problems with modeling the task in this way. They suggest areas for future research.