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.
In terms of modeling approaches, in the context of extractive summarization, REF model content structure (i.e., the order in which topics appear) in documents.
Catching The Drift: Probabilistic Content Models, With Applications To Generation And Summarization
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science