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JCR Impact Factor: 0.650
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Issues per year: 4
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Next issue: Feb 2020
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Supporting Location Transparent Services in a Mobile Edge Computing Environment, GILLY, K., FILIPOSKA, S., MISHEV, A.
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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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  3/2018 - 15

Rule-Based Turkish Text Summarizer (RB-TTS)

BIRANT, C. C. See more information about BIRANT, C. C. on SCOPUS See more information about BIRANT, C. C. on IEEExplore See more information about BIRANT, C. C. on Web of Science, AKTAS, O. See more information about AKTAS, O. on SCOPUS See more information about AKTAS, O. on SCOPUS See more information about AKTAS, O. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,113 KB) | Citation | Downloads: 201 | Views: 486

Author keywords
data processing, dictionaries. morphology, natural language processing, text processing

References keywords
turkish(10), text(6), language(5), summarization(4), information(4), extraction(4), evaluation(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 113 - 118
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03015
Web of Science Accession Number: 000442420900015
SCOPUS ID: 85052145263

Abstract
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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.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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