  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.
