Regina Barzilay and Mirella Lapata. 2005. Collective Content Selection for Concept-To-Text Generation. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing, 331-338. Vancouver.

A content selection component determines which information should be conveyed in the output of a natural language generation system. We present an ef - cient method for automatically learning content selection rules from a corpus and its related database. Our modeling framework treats content selection as a collective classi cation problem, thus allowing us to capture contextual dependencies between input items. Experiments in a sports domain demonstrate that this approach achieves a substantial improvement over context-agnostic methods.

@InProceedings{Barzilay:Lapata:05b,
  author =       {Regina Barzilay and Mirella Lapata},
  title =        {Collective Content Selection for Concept-To-Text Generation},
  crossref =     {EMNLP:05},
  pages =        {331--338}
}

@Proceedings{EMNLP:05,
  title =        {Proceedings of the HLT/EMNLP},
  booktitle =    {Proceedings of the HLT/EMNLP},
  address =      {Vancouver},
  year =         2005
}