Quarterly (March, June, September, December)
160 pp. per issue
6 3/4 x 10
ISSN
0891-2017
E-ISSN
1530-9312
2014 Impact factor:
1.23

Computational Linguistics

Paola Merlo, Editor
December 2011, Vol. 37, No. 4, Pages 811-842
(doi: 10.1162/COLI_a_00077)
© 2011 Association for Computational Linguistics
Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries
Article PDF (391.64 KB)
Abstract

Although there has been much theoretical work on using various information status distinctions to explain the form of references in written text, there have been few studies that attempt to automatically learn these distinctions for generating references in the context of computer-regenerated text. In this article, we present a model for generating references to people in news summaries that incorporates insights from both theory and a corpus analysis of human written summaries. In particular, our model captures how two properties of a person referred to in the summary—familiarity to the reader and global salience in the news story—affect the content and form of the initial reference to that person in a summary. We demonstrate that these two distinctions can be learned from a typical input for multi-document summarization and that they can be used to make regeneration decisions that improve the quality of extractive summaries.