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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
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Next issue: Feb 2019
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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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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  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: 119 | Views: 153

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.


References | Cited By  «-- Click to see who has cited this paper

[1] Oflazer K. & Kuruoz, I., "Tagging and morphological disambiguation of Turkish text", Proceedings of the Fourth Conference on Applied Natural LanguaProcessing, October 13-15, Stuttgart, Germany, 1994.
[CrossRef]


[2] Tur G., Hakkani-Tur D. & Oflazer, K., "A statistical information extraction system for Turkish". Natural Language Engineering, 9, pp. 181-210, 2003.
[CrossRef] [SCOPUS Times Cited 44]


[3] Bilgin O., Cetinoglu O. & Oflazer K., "Building a wordnet for Turkish," Romanian Journal of Information Science and Technology, 7 (1-2). pp. 163-172, 2004.

[4] Karakaya K. M. & Guvenir H. A., "ARG: A Tool for Automatic Report Generation", Istanbul University - Journal of Electrical & Electronics Engineering, Vol. 4, No. 2, pp. 1101-1109, 2004.

[5] Amasyali, M. F. & Diri, B., "Automatic turkish text categorization in terms of author, genre and gender". NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems, pp. 221-226, 2006.
[CrossRef]


[6] Ercan, G., "Automated Text Summarization and Keyphrase Extraction". Unpublished MSc thesis, Bilkent University, 2006.

[7] Ercan, G. & Cicekli, I., "Using lexical chains for Keyword Extraction". Information Processing and Management, 43, pp. 1705-1714, 2007.
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 117]


[8] Kutlu, M., Cigir, C. & Cicekli, I. "Generic Text Summarization in Turkish". The Computer Journal, 53: 8, pp. 1315-1323, 2010.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 16]


[9] Ozsoy, M. G., Cicekli, I. & Alpaslan, F. N., "Text summarization of Turkish texts using latent semantic analysis". Proceedings of the 23rd International Conference on Computational Linguistics, COLING'10, pp. 869-876, 2010.

[10] Uzun-Per, M., "Developing a Concept Extraction System for Turkish". Unpublished MSc. Thesis, Bogazici University, 2011.

[11] Demir, S., Durgar El-Kahlout, I., Unal, E. & Kaya, H., "Turkish Paraphrase Corpus". Proceedings of the Eight International Conference on Language Resources and Evaluation LREC'12. pp. 4087-4091, 2012.

[12] Aktas, O. & Cebi, Y., "Rule-Based Sentence Detection Method (RBSDM) for Turkish", International Journal of Language and Linguistics, 1 (1), 1-6, 2013.
[CrossRef]


[13] Hyland, K., "Persuasion and context: The pragmatics of academic metadiscourse". Journal of Pragmatics. 30: 437-455, 1998.
[CrossRef] [Web of Science Times Cited 188]


[14] Liu, F. & Liu, Y., "Exploring Correlation between ROUGE and Human Evaluation in Meeting Summaries". IEEE Transactions On Audio, Speech, and Language Processing, 2009.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 13]


[15] Lin C.-Y., "ROUGE: a package for automatic evaluation of summaries". In Moens, M. F. & Szpakowicz, S. (eds.), Workshop Text Summarization Branches Out (ACL '04), ACL, Barcelona, Spain, pp. 74-81, July 2004.

[16] Doran, W. P., Stokes, N., Dunnion, J. & Carthy, J., "Comparing lexical chain-based summarisation approaches using an extrinsic evaluation". In Proceedings of the Global Wordnet Conference (GWC 2004).

[17] Birant, C. C., "Root-Suffix seperation of Turkish words". M.Sc. Thesis. Izmir: Dokuz Eylul Universitesi, 2009.



References Weight

Web of Science® Citations for all references: 288 TCR
SCOPUS® Citations for all references: 190 TCR

Web of Science® Average Citations per reference: 16 ACR
SCOPUS® Average Citations per reference: 11 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-12-10 11:31 in 61 seconds.




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


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