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

跨多个文本源的信息融合的一个共同的理论是发展。开展了三项主要任务：（a）开发用于识别任意域中的相关文本文档集合的结构的鲁棒技术，并将其用于产生文档集合的图形表示，（B）创建用户可以指定其摘要偏好的环境，以及（c）应用基于图的方法来基于用户的优先级产生相关文档的聚类的个性化多文档摘要。跨文档结构基于诸如释义、矛盾、视角变化和互补等特征。一个大规模的跨文档链接的分类正在调查。为用户提供大量关键文本信息的个性化摘要有望加快或促进他们访问Web。计划大规模部署基于跨文档结构的基于Web的摘要系统，预计将有数百万用户使用。

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.