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

 Regina Barzilay, Lillian Lee


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-proposedmethods.


The source code for this work can be downloaded from the link below.

Source code