Paper: Statistical Acquisition Of Content Selection Rules For Natural Language Generation

ACL ID W03-1016
Title Statistical Acquisition Of Content Selection Rules For Natural Language Generation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003
Authors

A Natural Language Generation system produces text using as input semantic data. One of its very first tasks is to decide which pieces of information to convey in the output. This task, called Content Se- lection, is quite domain dependent, requir- ing considerable re-engineering to trans- port the system from one scenario to an- other. In this paper, we present a method to acquire content selection rules automat- ically from a corpus of text and associated semantics. Our proposed technique was evaluated by comparing its output with in- formation selected by human authors in unseen texts, where we were able to fil- ter half the input data set without loss of recall.