Text summarization of turkish texts using latent semantic analysis

Date
2010
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Source Title
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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Publisher
ACM
Volume
2
Issue
Pages
869 - 876
Language
English
Type
Conference Paper
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Abstract

Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algorithms produces the best scores.

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Keywords
Latent Semantic Analysis, Text data, Text summarization, Turkish texts, Turkishs, Computational linguistics, Semantics, Algorithms
Citation
Published Version (Please cite this version)