Author

Steven Cotter

Defense Date

3-22-2012

Graduation Date

2012

Availability

Immediate Access

Submission Type

thesis

Degree Name

MS

Department

Computational Mathematics

School

McAnulty College and Graduate School of Liberal Arts

Committee Chair

Patrick Juola

Committee Member

John Kern

Committee Member

Donald Simon

Keywords

Automatic summarization, Extractive summarization, Search engine precision, Sentence clustering, Spectral clustering, TextRank

Abstract

Have you ever searched for something on the web and been overloaded with irrelevant results? Many search engines tend to cast a very wide net and rely on ranking to show you the relevant results first. But, this doesn't always work. Perhaps the occurrence of irrelevant results could be reduced if we could eliminate the unimportant content from each webpage while indexing. Instead of casting a wide net, maybe we can make the net smarter. Here, I investigate the feasibility of using automated document summarization and clustering to do just that. The results indicate that such methods can make search engines more precise, more efficient, and faster, but not without costs.

Format

PDF

Language

English

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