Test summarization using relevance measures and latent semantic analysis

US7607083B2 · kind B2 · utility

Assignee

• NEC CORPORATION · JP

Inventors

• Yihong Gong · Saratoga, US
• Xin Liu · Irvine, US

Key dates

• Filing date: Mar 26, 2001
• Grant date: Oct 20, 2009
• Expiry date: May 14, 2023

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y10S707/99943
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Text summarizers using relevance measurement technologies and latent semantic analysis techniques provide accurate and useful summarization of the contents of text documents. Generic text summaries may be produced by ranking and extracting sentences from original documents; broad coverage of document content and decreased redundancy may simultaneously be achieved by constructing summaries from sentences that are highly ranked and different from each other. In one embodiment, conventional Information Retrieval (IR) technologies may be applied in a unique way to perform the summarization; relevance measurement, sentence selection, and term elimination may be repeated in successive iterations. In another embodiment, a singular value decomposition technique may be applied to a terms-by-sentences matrix such that all the sentences from the document may be projected into the singular vector space; a text summarizer may then select sentences having the largest index values with the most important singular vectors as part of the text summary.