By Topic

Improving the informativeness of verbose queries using summarization techniques for spoken document retrieval

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shih-Hsiang Lin ; Comput. Sci. & Inf. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan ; Chen, B. ; Ea-Ee Jan

Query-by-example information retrieval aims at helping users to find relevant documents accurately when users provide specific query exemplars describing what they are interested in. The query exemplars are usually long and in the form of either a partial or even a full document. However, they may contain extraneous terms (or off-topic information) that would have a negative impact on the retrieval performance. In this paper, we propose to integrate extractive summarization techniques into the retrieval process so as to improve the informativeness of a verbose query exemplar. The original query exemplar is first divided into several sub-queries or sentences. To construct a new concise query exemplar, summarization techniques are then employed to select a salient subset of sub-queries. Experiments on the TDT Chinese collection show that the proposed approach is indeed effective and promising.

Published in:

Chinese Spoken Language Processing (ISCSLP), 2010 7th International Symposium on

Date of Conference:

Nov. 29 2010-Dec. 3 2010