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

Simple item page
dc.creatorBarzilay, Regina
dc.creatorLee, Lillian
dc.date2004-05-12
dc.date.accessioned2026-07-07T03:21:14Z
dc.date.available2026-07-07T03:21:14Z
dc.descriptionWe 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.
dc.descriptionBest paper award
dc.identifierhttps://arxiv.org/abs/cs/0405039
dc.identifierhttp://arxiv.org/abs/cs/0405039
dc.identifierHLT-NAACL 2004: Proceedings of the Main Conference, pp. 113--120
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32121
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleCatching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
dc.typetext

Files

Collections

arXiv