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Modeling Medical Content for Automated Summarization
Author(s) -
JOHNSON DAVID B.,
ZOU QINGHUA,
DIONISIO JOHN D.,
LIU VICTOR ZHENYU,
CHU WESLEY W.
Publication year - 2002
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2002.tb04901.x
Subject(s) - automatic summarization , computer science , information retrieval , multi document summarization , variety (cybernetics) , set (abstract data type) , point (geometry) , content (measure theory) , focus (optics) , key (lock) , world wide web , artificial intelligence , mathematical analysis , physics , geometry , mathematics , computer security , optics , programming language
A bstract : 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.