Improving Search Results with Automated Summarization and Sentence Clustering

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2012

Author dc:creator

• Cotter, Steven

Contributors dc:contributor

• Patrick Juola
• John Kern
• Donald Simon

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

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/434

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1447