Johnson David B, Zou Qinghua, Dionisio John D, Liu Victor Zhenyu, Chu Wesley W
Department of Radiological Sciences, University of California Los Angeles, 90024, USA.
Ann N Y Acad Sci. 2002 Dec;980:247-58. doi: 10.1111/j.1749-6632.2002.tb04901.x.
Medical information is available from a variety of new online resources. Given the number and diversity of sources, methods must be found that will enable users to quickly assimilate and determine the content of a document. Summarization is one such tool that can help users to quickly determine the main points of a document. Previous methods to automatically summarize text documents typically do not attempt to infer or define the content of a document. Rather these systems rely on secondary features or clues that may point to content. This paper describes text summarization techniques that enable users to focus on the key content of a document. The techniques presented here analyze groups of similar documents in order to form a content model. The content model is used to select sentences forming the summary. The technique does not require additional knowledge sources; thus the method should be applicable to any set of text documents.
医学信息可从各种新的在线资源中获取。鉴于来源的数量和多样性，必须找到能够让用户快速吸收并确定文档内容的方法。摘要就是这样一种工具，它可以帮助用户快速确定文档的要点。以前自动总结文本文件的方法通常不会尝试推断或定义文档的内容。相反，这些系统依赖于可能指向内容的次要特征或线索。本文描述了一些文本摘要技术，这些技术能让用户专注于文档的关键内容。这里介绍的技术通过分析相似文档组来形成一个内容模型。该内容模型用于选择构成摘要的句子。该技术不需要额外的知识来源；因此该方法应该适用于任何一组文本文件。
