Generic text summarization for Turkish

Date
2010
Authors
Kutlu, M.
Cığır, C.
Cicekli, I.
Advisor
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Instructor
Source Title
The Computer Journal
Print ISSN
0010-4620
Electronic ISSN
Publisher
Oxford University Press
Volume
53
Issue
8
Pages
1315 - 1323
Language
English
Type
Article
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Abstract

In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores. Sentence scores are calculated using their surface-level features, and summaries are created by extracting the highest ranked sentences from the original documents. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, we use features such as term frequency, key phrase (KP), centrality, title similarity and sentence position. The sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine-learning techniques with the help of human-constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries of two newly created Turkish data sets. This paper presents one of the first Turkish summarization systems, and its results are promising. We introduce the usage of KP as a surface-level feature in text summarization, and we show the effectiveness of the centrality feature in text summarization. The effectiveness of the features in Turkish text summarization is also analyzed in detail. © The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

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Keywords
Natural language processing, Summary extraction, Text summarization, Data sets, Feature weight, Key-phrase, Machine learning techniques, Performance evaluation, Score function, Summarization systems, Summary extraction, Term frequency
Citation
Published Version (Please cite this version)