Collective Content Selection for Concept-to-Text Generation.

Regina Barzilay, Maria Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A content selection component determines which information should be conveyed in the output of a natural language generation system. We present an efficient method for automatically learning content selection rules from a corpus and its related database. Our modeling framework treats content selection as a collective classification 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.
Original languageEnglish
Title of host publicationProceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP),
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 2005


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