Paper: Automatic Single-Document Key Fact Extraction from Newswire Articles

ACL ID E09-1048
Title Automatic Single-Document Key Fact Extraction from Newswire Articles
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

This paper addresses the problem of ex- tracting the most important facts from a news article. Our approach uses syntac- tic, semantic, and general statistical fea- tures to identify the most important sen- tences in a document. The importance of the individual features is estimated us- ing generalized iterative scaling methods trained on an annotated newswire corpus. The performance of our approach is evalu- ated against 300 unseen news articles and shows that use of these features results in statistically significant improvements over a provenly robust baseline, as measured using metrics such as precision, recall and ROUGE.