Inferring Strategies for Sentence Ordering in Multidocument News Summarization

TL;DR

This paper explores strategies for organizing information in multidocument news summarization to improve coherence, combining chronological and topical constraints, and demonstrates significant improvements over baseline methods.

Contribution

It introduces a methodology for studying and implementing sentence ordering strategies in multidocument summarization, focusing on news articles.

Findings

Enhanced ordering algorithm outperforms baseline strategies

Combining chronological and topical constraints improves coherence

Developed a corpus of multiple acceptable orderings for evaluation

Abstract

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.