2004/05/12 by Regina Barzilay, Lillian Lee · 2 citations
Computer Science · #cs.CL
published as HLT-NAACL 2004: Proceedings of the Main Conference, pp. 113--120 · Best paper award
arxiv created 2004/05/12 · arxiv updated 2009/12/01
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.