Regina Barzilay and Mirella Lapata. 2006. Aggregation via Set Partitioning for Natural Language Generation. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, 359--366. New York, NY.

The role of aggregation in natural language generation is to combine two or more linguistic structures into a single sentence. The task is crucial for generating concise and readable texts. We present an efficient algorithm for automatically learning aggregation rules from a text and its related database. The algorithm treats aggregation as a set partitioning problem and uses a global inference procedure to find an optimal solution. Our experiments show that this approach yields substantial improvements over a clustering-based model which relies exclusively on local information.

@InProceedings{Barzilay:Lapata:06,
  author =       {Regina Barzilay and Mirella Lapata},
  title =        {Aggregation via Set Partitioning for Natural
                  Language Generation},  
  crossref =     {HLT:NAACL:06},
  pages =        {359--366}
}

@Proceedings{HLT:NAACL:06,
  title =        {Proceedings of the Human Language Technology
                  Conference of the North American Chapter of the Association of
                  Computational Linguistics},  
  booktitle =    {Proceedings of the Human Language Technology
                  Conference of the North American Chapter of the Association for
                  Computational Linguistics},   
  address =      {New York, NY},
  year =         2006
}