Paper: Collective Content Selection For Concept-To-Text Generation

ACL ID H05-1042
Title Collective Content Selection For Concept-To-Text Generation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

A content selection component deter- mines which information should be con- veyed in the output of a natural language generation system. We present an effi- cient method for automatically learning content selection rules from a corpus and its related database. Our modeling frame- work treats content selection as a col- lective classification problem, thus allow- ing us to capture contextual dependen- cies between input items. Experiments in a sports domain demonstrate that this approach achieves a substantial improve- ment over context-agnostic methods.