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