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.
In REF , the content selection is treated as a collective classification problem which allows the system to capture contextual dependencies between input data items.
Collective Content Selection For Concept-To-Text Generation
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science