Collective content selection for concept-to-text generation

This work presents an efficient method for automatically learning content selection rules from a corpus and its related database and treats content selection as a collective classification problem, thus allowing it to capture contextual dependencies between input items.

Abstract

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.