Text Summarization of Turkish Texts using Latent Semantic Analysis

Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pages 869–876, Beijing, August 2010 Text Summarization of Turkish Texts using Latent Semantic Analysis Makbule Gulcin Ozsoy Dept. of Computer Eng. Middle East Tech. Univ. e1395383@ceng.metu.edu.tr Ilyas Cicekli Dept. of Computer Eng. Bilkent University ilyas@cs.bilkent.edu.tr Ferda Nur Alpaslan Dept. of Computer Eng. Middle East Tech. Univ. alpaslan@ceng.metu.edu.tr Abstract Text summarization solves the problem of extracting important information from huge amount of text data. There are vari- ous 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 summariza- tion algorithms are explained and two new LSA based summarization algo- rithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algo- rithms produces the best scores. 1 Introduction The exponential growth in text documents brings the problem of finding out whether a text docu- ment meets the needs of a user or not. In order to solve this problem, text summarization systems which extract brief information from a given text are created. By just looking at the summary of a document, a user can decide whether the docu- ment is of interest to him/her without looking at the whole document. The aim of a text summarization system is to generate a summary for a given document such that the generated summary contains all neces- sary information in the text, and it does not in- clude redundant information. Summaries can have different forms (Hahn and Mani, 2000). Extractive summarization systems collect impor- tant sentences from the input text in order to generate summaries. Abstractive summarization systems do not collect sentences from the input text, but they try to capture the main concepts in the text, and generate new sentences to represent these main concepts. Abstractive summarization approach is similar to the way that human sum- marizers follow. Since creating abstractive summaries is a more complex task, most of automatic text summarization systems are ex- tractive summarization systems. Summarization methods can be categorized according to what they generate and how they generate it (Hovy and Lin, 1999). A summary can be extracted from a single document or from multiple documents. If a summary is generated from a single document, it is known as single- document summarization. On the other hand, if a single summary is generated from multiple documents on the same subject, this is known as multi-document summarization. Summaries are also categorized as generic summaries and query-based summaries. Generic summarization systems generate summaries containing main topics of documents. In query-based summariza- tion, the generated summaries contain the sen- tences that are related to the given queries. Extractive summarization systems determine the important sentences of the text in order to put them into the summary. The important sen- tences of the text are the sentences that represent the main topics of the text. Summarization sys- tems use different approaches to …

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