ITR: Information Fusion Across Multiple Text Sources: A Common Theory

一个跨多个文本源的信息融合的通用理论将被开发出来。主要有三个任务：(a)开发了用于识别任意领域中相关文本文档集的结构的健壮技术，并用于生成文档集的图形表示，(b)创建了用户可以指定其摘要偏好的环境，以及(c)应用基于图的方法根据用户的优先级生成相关文档簇的个性化多文档摘要。跨文档结构基于释义、矛盾、视角变化和补充等特征。正在研究跨文件链接的大规模分类法。为用户提供大量关键文本信息的个性化摘要有望加快用户访问Web的速度，并为其提供便利。计划大规模部署基于跨文档结构的基于web的摘要系统，预计将被数百万用户使用。

A common theory of information fusion across multiple text sources is to be developed. Three main tasks are undertaken: (a) robust techniques for identifying structure across sets of related textual documents in arbitrary domains are developed and used to produce graph representations of the document sets, (b) an environment in which users can specify their summarization preferences is created, and (c) graph-based methods are applied to produce personalized multi-document summaries of clusters of the related documents based on the users' priorities. Cross-document structure is based on features such as paraphrasing, contradiction, change of perspective, and complementation. A large-scale taxonomy of cross-document links is being investigated. Providing users with personalized abstracts of large amounts of critical textual information is expected to speed up and otherwise facilitate their access to the Web. Large-scale deployment of a Web-based summarization system based on cross-document structure is planned and is expected to be used by millions of users.