Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
<|reference_start|>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 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.<|reference_end|>
@article{barzilay2004catching,
    title={Catching the Drift: Probabilistic Content Models, with Applications to
  Generation and Summarization},
    author={Regina Barzilay and Lillian Lee},
    journal={HLT-NAACL 2004: Proceedings of the Main Conference, pp. 113--120},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0405039},
    primaryClass={cs.CL}
}