title: Modeling medical content for automated summarization.
abstract: 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.
authors.affiliation: Department of Radiological Sciences, University of California Los Angeles, 90024, USA. djohnson@itmedicine.net
authors.forename: David B
authors.initials: DB
authors.lastname: Johnson
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Zou
authors.forename: John D
authors.initials: JD
authors.lastname: Dionisio
authors.forename: Victor Zhenyu
authors.initials: VZ
authors.lastname: Liu
authors.forename: Wesley W
authors.initials: WW
authors.lastname: Chu
mesh_terms: D001331:Automation; D016000:Cluster Analysis; D003463:Cues; D004282:Documentation / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval; D008490:Medical Informatics