Inferring Strategies for Sentence Ordering in Multidocument News Summarization

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.

Proceedings of the 2nd Workshop on Argumentation Mining

“…Arranging sentences by order of publication dates of source documents is known as the chronological ordering (Barzilay et al, 2002). It is effective for news article summarization because descriptions of a certain event tend to follow the order of publication.…”

“…To the best of our knowledge, there is no research that examines the claim-support structure of debate texts for the sentence ordering problem. Most of the previous works on sentence ordering (Barzilay et al, 2002;Lapata, 2003;Bollegala et al, 2006;Tan et al, 2013) focus on the sentence order of news articles and do not consider the structures of arguments. These methods mingle claim and supportive sentences together, which decreases the persuasiveness of generated opinions.…”

Untitled

“…The process is needed to be performed in order to obtain the composition of the summary's sentences that allows users to get information easily. Several summary's sentences ordering methods had been proposed in [5][6][7]. It considers a variety of approaches, i.e.…”

Automatic Summarization from Multiple Documents

“…It has been shown that reordering can prove useful [BEM02]. In [BEM02] two ordering strategies are presented, namely the Majority Ordering (MO) and the Chronological Ordering (CO) algorithm.…”

“…This problem is very evident when, for instance, evaluating anaphora resolution on the final summary. Another important aspect of this problem is sentence ordering in the output summary [BEM02]. In [HLY + 06] authors prove that the strategy of sentence ordering can offer improvement to (mainly bad) summaries.…”