@InProceedings{daume06bqfs,
  author =       {Hal {Daum\'e III} and Daniel Marcu},
  title =        {Bayesian Query-Focused Summarization},
  booktitle =    {Proceedings of the Conference of the Association for Computational Linguistics (ACL)},
  year =         {2006},
  address =      {Sydney, Australia},
  abstract =     {
    We present BayeSum (for ``Bayesian summarization''), a model for
    sentence extraction in query-focused summarization.  BayeSum
    leverages the common case in which multiple documents are relevant
    to a single query.  Using these documents as reinforcement for
    query terms, BayeSum is not afflicted by the paucity of
    information in short queries.  We show that approximate inference
    in BayeSum is possible on large data sets and results in a
    state-of-the-art summarization system.  Furthermore, we show how
    BayeSum can be understood as a justified query expansion technique
    in the language modeling for IR framework.
  },
  keywords = {nlp bayes sum},
  tagline = {We describe the BayeSum system (previously called BQFS in our DUC and MSE papers) for query-focused sentence extraction in a Bayesian framework (the model looks like a topic model if you squint a little). Achieves competitive performance on white-box experiments and also leads to good systems in DUC. Also, from an IR perspective, provides a formalism for statistically grounded query expansion. Slides available as <a href="http://pub.hal3.name/daume06bqfs.odp">OpenOffice</a> and <a href="http://pub.hal3.name/daume06bqfs.odp.pdf">PDF</a>.},
  url = {http://pub.hal3.name/#daume06bqfs}
}