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.

• Type: Conference
• Year: 2004
• Where: NAACL
• Authors: Regina Barzilay, Lillian Lee
• Keywords: Content Models, Effective Knowledge-lean Method, Hidden Markov Models, NAACL 2004, NAACL 2007