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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/7212
Title: Automatic Text Decomposition Using Text Segments and Text Themes
Authors: Salton, Gerard
Singhal, Amit
Buckley, Chris
Mitra, Mandar
Keywords: computer science
technical report
Issue Date: Nov-1995
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR95-1555
Abstract: With the widespread use of full-text information retrieval, passage-retrieval techniques are becoming increasingly popular. Larger texts can then be replaced by important text excerpts, thereby simplifying the retrieval task and improving retrieval effectiveness. Passage-level evidence about the use of words in local contexts is also useful for resolving language ambiguities and improving retrieval output. Two main text decomposition strategies are introduced in this study, including a chronological decomposition into {\em text segments}, and semantic decomposition into {\em text themes}. The interaction between text segments and text themes is then used to characterize text structure, and to formulate specifications for information retrieval, text traversal, and text summarization.
URI: http://hdl.handle.net/1813/7212
Appears in Collections:Computer Science Technical Reports

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