Inferring strategies for sentence ordering in multidocument news summarization
Inferring strategies for sentence ordering in multidocument news summarization
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DOI：
10.1613/jair.991
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发表时间：
2002-01-01
影响因子：
5
通讯作者：
McKeown, KR
中科院分区：
文献类型：
--
作者：
Barzilay, R;Elhadad, N;McKeown, KR
组织多文档摘要信息以使生成的摘要一致的问题相对较少受到关注。虽然单文档摘要的句子顺序可以根据输入文章中句子的顺序确定，但多文档摘要的情况并非如此，其中摘要句子可以从不同的输入文章中得出。在本文中，我们提出了一种研究新闻类型中排序信息属性的方法，并描述了在我们为该任务开发的多个可接受排序的语料库上进行的实验。基于这些实验，我们实施了一种信息排序策略，该策略结合了事件时间顺序和主题相关性的约束。对我们的增强算法的评估表明，与两种基线策略相比，排序有了显着改进。
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.