Rule-Based Turkish Text Summarizer (RB-TTS)

The volume of data produced has exponentially increased with the digital revolution and it continues to race to the limits of the capacity of our computers and supercomputers. Automatic text summarization is one of efforts to tame the bestial product of our daily data production, which have gener...

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Main Authors:	BIRANT, C. C., AKTAS, O.
Format:	Article
Language:	English
Published:	Stefan cel Mare University of Suceava 2018-08-01
Series:	Advances in Electrical and Computer Engineering
Subjects:	data processing dictionaries. morphology natural language processing text processing
Online Access:	http://dx.doi.org/10.4316/AECE.2018.03015

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recordtype	oai_dc
spelling	doaj-art-1872de32245048d999ad5c694f203eb72018-09-08T07:02:34ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002018-08-0118311311810.4316/AECE.2018.03015Rule-Based Turkish Text Summarizer (RB-TTS)BIRANT, C. C.AKTAS, O.The volume of data produced has exponentially increased with the digital revolution and it continues to race to the limits of the capacity of our computers and supercomputers. Automatic text summarization is one of efforts to tame the bestial product of our daily data production, which have generated the 90 percent of the data ever produced by humans, in the last two years. In order to understand what a text is about, a summary is needed which is short enough not to compromise the understandability, and comprehensive to include the most important topics of that text. Numerous automatic text summarization software which aimed at achieving this goal use semantic relations, thesauri, and word frequency lists. In this paper, development phases and evaluation results of a software tool called Rule Based Turkish Text Summarizer (RB-TTS) are presented. The average success rate of the RB-TTS is analyzed both quantitatively using ROUGE-N metrics and qualitatively. In the qualitative analysis, five summaries, obtained automatically from texts, are evaluated by 10 Ph.D. students from Dokuz Eylul University Department of Linguistics. The summaries generated by RB-TTS software are compared with the summaries, which were written by the authors of the corresponding texts, and marked as close to them.http://dx.doi.org/10.4316/AECE.2018.03015data processingdictionaries. morphologynatural language processingtext processing
institution	Open Data Bank
collection	Open Access Journals
building	Directory of Open Access Journals
language	English
format	Article
author	BIRANT, C. C. AKTAS, O.
spellingShingle	BIRANT, C. C. AKTAS, O. Rule-Based Turkish Text Summarizer (RB-TTS) Advances in Electrical and Computer Engineering data processing dictionaries. morphology natural language processing text processing
author_facet	BIRANT, C. C. AKTAS, O.
author_sort	BIRANT, C. C.
title	Rule-Based Turkish Text Summarizer (RB-TTS)
title_short	Rule-Based Turkish Text Summarizer (RB-TTS)
title_full	Rule-Based Turkish Text Summarizer (RB-TTS)
title_fullStr	Rule-Based Turkish Text Summarizer (RB-TTS)
title_full_unstemmed	Rule-Based Turkish Text Summarizer (RB-TTS)
title_sort	rule-based turkish text summarizer (rb-tts)
publisher	Stefan cel Mare University of Suceava
series	Advances in Electrical and Computer Engineering
issn	1582-7445 1844-7600
publishDate	2018-08-01
description	The volume of data produced has exponentially increased with the digital revolution and it continues to race to the limits of the capacity of our computers and supercomputers. Automatic text summarization is one of efforts to tame the bestial product of our daily data production, which have generated the 90 percent of the data ever produced by humans, in the last two years. In order to understand what a text is about, a summary is needed which is short enough not to compromise the understandability, and comprehensive to include the most important topics of that text. Numerous automatic text summarization software which aimed at achieving this goal use semantic relations, thesauri, and word frequency lists. In this paper, development phases and evaluation results of a software tool called Rule Based Turkish Text Summarizer (RB-TTS) are presented. The average success rate of the RB-TTS is analyzed both quantitatively using ROUGE-N metrics and qualitatively. In the qualitative analysis, five summaries, obtained automatically from texts, are evaluated by 10 Ph.D. students from Dokuz Eylul University Department of Linguistics. The summaries generated by RB-TTS software are compared with the summaries, which were written by the authors of the corresponding texts, and marked as close to them.
topic	data processing dictionaries. morphology natural language processing text processing
url	http://dx.doi.org/10.4316/AECE.2018.03015
_version_	1612610907889729536

Rule-Based Turkish Text Summarizer (RB-TTS)

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