Paper: Supervised And Unsupervised Learning For Sentence Compression

ACL ID P05-1036
Title Supervised And Unsupervised Learning For Sentence Compression
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

In Statistics-Based Summarization - Step One: Sentence Compression, Knight and Marcu (Knight and Marcu, 2000) (K&M) present a noisy-channel model for sen- tence 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 re- search.