Inferring Strategies for Sentence Ordering in Multidocument News Summarization

McKeown, Kathleen; Barzilay, Regina; Elhadad, Noemie

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.



  • thumnail for application/pdf 217 KB Download File

Also Published In

Journal of Artificial Intelligence Research

More About This Work

Academic Units
Computer Science
Published Here
April 8, 2013