期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：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.
