By Topic

Corpus Based Extractive Document Summarization for Indic Script

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
$31 $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)
Reddy, P.V. ; Dept. of CSE, Raja Mahendra Eng. Coll., Hyderabad, India ; Vardhan, B.V. ; Govardhan, A.

Summarization is a process of generating condensed form of a given text document, which retains its information and overall meaning. Document summarization approaches are broadly classified into two i.e. extractive summarization approach and abstractive summarization approach. In this paper, we performed single document summarization to generate summary of Telugu text document by using extractive summarization approach. Though there are many document surface features exists, we consider those features which can extensively cover original document and generates summary with less redundancy. We considered the features such as sentence position, sentence similarity with the title, centrality of the sentence and word frequency. To increase the strength of the features, we used a corpus which contains 3000 documents and performed various preprocessing steps like stop word elimination and stemming to retain more meaningful words within the sentence. Sentences are ranked by calculating the scores for each individual sentence by considering all four features simultaneously with optimum weights. The optimum weights to the feature are learned with the help human constructed summaries. The machine generated summaries are evaluated using F1 measure followed by human judgements.

Published in:

Asian Language Processing (IALP), 2011 International Conference on

Date of Conference:

15-17 Nov. 2011