@inproceedings{galley2007lexicalized,
author = {Galley, Michel and McKeown, Kathleen},
title = {Lexicalized Markov Grammars for Sentence Compression},
booktitle = {Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)},
year = {2007},
month = {April},
abstract = {We present a sentence compression system based on synchronous context-free grammars (SCFG), following the successful noisy-channel approach of (Knight and Marcu, 2000). We define a head-driven Markovization formulation of SCFG deletion rules, which allows us to lexicalize probabilities of constituent deletions. We also use a robust approach for tree-to-tree alignment between arbitrary document-abstract parallel corpora, which lets us train lexicalized models with much more data than previous approaches relying exclusively on scarcely available document-compression corpora. Finally, we evaluate different Markovized models, and find that our selected best model is one that exploits head-modifier bilexicalization to accurately distinguish adjuncts from complements, and that produces sentences that were judged more grammatical than those generated by previous work.},
url = {http://approjects.co.za/?big=en-us/research/publication/lexicalized-markov-grammars-sentence-compression/},
edition = {Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL)},
}