Defense Date
3-22-2012
Graduation Date
Spring 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
Language
English
Recommended Citation
Cotter, S. (2012). Improving Search Results with Automated Summarization and Sentence Clustering (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/434