Catching the drift: Probabilistic content models, with applications to generation and summarization
Regina Barzilay and Lillian Lee
Proceedings of HLT-NAACL, pp. 113--120, 2004. Best paper award

We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. We first present an effective knowledge-lean method for learning content models from un-annotated documents, utilizing a novel adaptation of algorithms for Hidden Markov Models. We then apply our method to two complementary tasks: information ordering and extractive summarization. Our experiments show that incorporating content models in these applications yields substantial improvement over previously-proposed methods.

Recognition

@inproceedings{Barzilay+Lee:04a, author = {Regina Barzilay and Lillian Lee}, title = {Catching the drift: Probabilistic content models, with applications to generation and summarization}, year = {2004}, pages = {113--120}, booktitle = {Proceedings of HLT-NAACL} }

This paper is based upon work supported in part by the National Science Foundation under grants ITR/IM IIS-0081334 and IIS-0329064 and by an Alfred P. Sloan Research Fellowship. Any opinions, findings, and conclusions or recommendations expressed above are those of the authors and do not necessarily reflect the views of the National Science Foundation or Sloan Foundation.

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