Sciweavers

Share
NAACL
2004

Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization

3 years 9 months ago
Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
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 unannotated 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.
Regina Barzilay, Lillian Lee
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NAACL
Authors Regina Barzilay, Lillian Lee
Comments (0)
books