Automatic Single-Document Key Fact Extraction from Newswire Articles
Itamar Kastner and Christof Monz
In: EACL 2009, 30 Mar - 02 Apr, 2009, Greece.
This paper addresses the problem of extracting the most important
facts from a news article. Our approach uses syntactic, semantic, and
general statistical features to identify the most important
sentences in a document. The importance of the individual features
is estimated using generalized iterative scaling methods trained on
an annotated newswire corpus. The performance of our approach is
evaluated 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.