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Extractive Text Summarization System for News Texts

Yıl 2020, Cilt 8, Sayı 4, 179 - 184, 31.12.2020
https://doi.org/10.18100/ijamec.800905

Öz

In today's conditions, it is difficult to obtain information quickly and efficiently due to the size of the data. There are various text documents on the internet and a good extraction algorithm is essential to have the most relevant information from them. Long texts can be boring sometimes. So, readers are eager to get the main idea of the text or any useful information. For this reason, the importance of automatic summarization systems is understood. Text summarization systems can be considered as abstractive summarization or extractive summarization. While abstractive systems produce a summary with new sentences, extractive systems make a selection of sentences from the text used and combine them and present them as a summary. Creating a successful summarization algorithm increases in direct proportion to the success of applying text mining techniques. Text summary systems provide a summary of the text to the user by scoring words and sentences in the main text using various methods and combining high ranked sentences as a result of the process. In this context, many scoring methods have been used. In our study, news data sets are used. The algorithm used is based on extraction and has been evaluated using a task-independent method. After evaluation, the two highest scores taken are ROUGE-1 with 0.68 score and ROUGE-S with 0.54 score. Through all evaluation steps, Precision, Recall and F-Measure values are also specified to see the steps clearly.

Kaynakça

  • T. Sri Rama Raju and Bhargav Allarpu, Text Summarization using Sentence Scoring Method. April 2017. Volume: 04 Issue: 04 | pages 1777-1779
  • S.A. Babar and Pallavi D. Patil, Improving Performance of Text Summarization. Procedia Computer Science 46, 2015. 354 – 363, (ICICT 2014)
  • Lin, C.Y., ROUGE: A Package for Automatic Evaluation of Summaries. Spain, In Proceedings of the Workshop on Text Summarization Branches Out, 25 – 26 July 2004.
  • Josef Steinberger and Karel Ježek, Evaluatıon Measures For Text Summarızation. Computing and Informatics, March 2009, Vol. 28, 2009, 1001–1026.
  • Aysun Güran, Otomatik Metin Özetleme Sistemi. Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2013
  • R. Satapathy and C. Guerreiro and I. Chaturvedi and E. Cambria, Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis. IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, 2017, pp. 407-413, doi: 10.1109/ICDMW.2017.59
  • Kaynar, O. and Işık, Y.E and Görmez, Y. and Demirkoparan F., Genetic Algorithm Based Sentence Extraction For Automatic Text Summarization. Yönetim Bilişim Sistemleri Dergisi, 2017, Cilt:3, Sayı:2, Sayfa:62-75, ISSN: 2148-3752
  • Lin, Ch. and Hovy, E., Automatic Evaluation of Summaries Using n-Gram Co-Occurrence Statistics. Canada, In Proceedings of HLT-NAACL, 2003.
  • https://www.ccs.neu.edu/home/vip/teach/DMcourse/5_topicmodel_summ/notes_slides/What-is-ROUGE.pdf
  • https://github.com/nltk/nltk/blob/develop/nltk/util.py#L53
  • Gündoğdu, Ö.E. and Duru, N., Türkçe Metin Özetlemede Kullanılan Yöntemler. Aydın, 18. Akademik Bilişim Konferansı, , 30 Ocak-5 Şubat 2016, Adnan Menderes Üniversitesi.
  • P. Yıldırım and M. Uludağ and A. Görür, Hastane Bilgi Sistemlerinde Veri Madenciliği. Çanakkale, Akademik Bilişim, Ocak 2008, Çanakkale Onsekiz Mart Üniversitesi.
  • A.A. Akın and M.D. Akın, Zemberek, An Open Source Nlp Framework For Turkic Languages. 2007, Structure 10, 1-5, 185.
  • K. Deniz and B. Fatma and O. Akin and Y. Fatih and B. Emin, Metin Madenciliği Kullanılarak Yazılım Kullanımına Dair Bulguların Elde Edilmesi. 2015.
  • S. Çelik, Metin Madenciliği ile Shakespeare Külliyatının İncelenmesi. MANAS Sosyal Araştırmalar Dergisi, 9(3), 1343-1357.
  • Moratanch, N. and S. Chitrakala, A Survey On Extractive Text Summarization. Chennai, ICCCSP, 2017, 1-6. 10.1109/ICCCSP.2017.7944061.
  • https://www.kaggle.com/pariza/bbc-news-summary

Yıl 2020, Cilt 8, Sayı 4, 179 - 184, 31.12.2020
https://doi.org/10.18100/ijamec.800905

Öz

Kaynakça

  • T. Sri Rama Raju and Bhargav Allarpu, Text Summarization using Sentence Scoring Method. April 2017. Volume: 04 Issue: 04 | pages 1777-1779
  • S.A. Babar and Pallavi D. Patil, Improving Performance of Text Summarization. Procedia Computer Science 46, 2015. 354 – 363, (ICICT 2014)
  • Lin, C.Y., ROUGE: A Package for Automatic Evaluation of Summaries. Spain, In Proceedings of the Workshop on Text Summarization Branches Out, 25 – 26 July 2004.
  • Josef Steinberger and Karel Ježek, Evaluatıon Measures For Text Summarızation. Computing and Informatics, March 2009, Vol. 28, 2009, 1001–1026.
  • Aysun Güran, Otomatik Metin Özetleme Sistemi. Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2013
  • R. Satapathy and C. Guerreiro and I. Chaturvedi and E. Cambria, Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis. IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, 2017, pp. 407-413, doi: 10.1109/ICDMW.2017.59
  • Kaynar, O. and Işık, Y.E and Görmez, Y. and Demirkoparan F., Genetic Algorithm Based Sentence Extraction For Automatic Text Summarization. Yönetim Bilişim Sistemleri Dergisi, 2017, Cilt:3, Sayı:2, Sayfa:62-75, ISSN: 2148-3752
  • Lin, Ch. and Hovy, E., Automatic Evaluation of Summaries Using n-Gram Co-Occurrence Statistics. Canada, In Proceedings of HLT-NAACL, 2003.
  • https://www.ccs.neu.edu/home/vip/teach/DMcourse/5_topicmodel_summ/notes_slides/What-is-ROUGE.pdf
  • https://github.com/nltk/nltk/blob/develop/nltk/util.py#L53
  • Gündoğdu, Ö.E. and Duru, N., Türkçe Metin Özetlemede Kullanılan Yöntemler. Aydın, 18. Akademik Bilişim Konferansı, , 30 Ocak-5 Şubat 2016, Adnan Menderes Üniversitesi.
  • P. Yıldırım and M. Uludağ and A. Görür, Hastane Bilgi Sistemlerinde Veri Madenciliği. Çanakkale, Akademik Bilişim, Ocak 2008, Çanakkale Onsekiz Mart Üniversitesi.
  • A.A. Akın and M.D. Akın, Zemberek, An Open Source Nlp Framework For Turkic Languages. 2007, Structure 10, 1-5, 185.
  • K. Deniz and B. Fatma and O. Akin and Y. Fatih and B. Emin, Metin Madenciliği Kullanılarak Yazılım Kullanımına Dair Bulguların Elde Edilmesi. 2015.
  • S. Çelik, Metin Madenciliği ile Shakespeare Külliyatının İncelenmesi. MANAS Sosyal Araştırmalar Dergisi, 9(3), 1343-1357.
  • Moratanch, N. and S. Chitrakala, A Survey On Extractive Text Summarization. Chennai, ICCCSP, 2017, 1-6. 10.1109/ICCCSP.2017.7944061.
  • https://www.kaggle.com/pariza/bbc-news-summary

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Fahrettin HORASAN (Sorumlu Yazar)
Dr., Kırıkkale University,
0000-0003-4554-9083
Türkiye


Burhan BİLEN
BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ
0000-0002-3106-7369
Türkiye

Yayımlanma Tarihi 31 Aralık 2020
Yayınlandığı Sayı Yıl 2020, Cilt 8, Sayı 4

Kaynak Göster

Bibtex @araştırma makalesi { ijamec800905, journal = {International Journal of Applied Mathematics Electronics and Computers}, issn = {2147-8228}, eissn = {2147-8228}, address = {}, publisher = {Selçuk Üniversitesi}, year = {2020}, volume = {8}, pages = {179 - 184}, doi = {10.18100/ijamec.800905}, title = {Extractive Text Summarization System for News Texts}, key = {cite}, author = {Horasan, Fahrettin and Bilen, Burhan} }
APA Horasan, F. & Bilen, B. (2020). Extractive Text Summarization System for News Texts . International Journal of Applied Mathematics Electronics and Computers , 8 (4) , 179-184 . DOI: 10.18100/ijamec.800905
MLA Horasan, F. , Bilen, B. "Extractive Text Summarization System for News Texts" . International Journal of Applied Mathematics Electronics and Computers 8 (2020 ): 179-184 <https://dergipark.org.tr/tr/pub/ijamec/issue/57538/800905>
Chicago Horasan, F. , Bilen, B. "Extractive Text Summarization System for News Texts". International Journal of Applied Mathematics Electronics and Computers 8 (2020 ): 179-184
RIS TY - JOUR T1 - Extractive Text Summarization System for News Texts AU - Fahrettin Horasan , Burhan Bilen Y1 - 2020 PY - 2020 N1 - doi: 10.18100/ijamec.800905 DO - 10.18100/ijamec.800905 T2 - International Journal of Applied Mathematics Electronics and Computers JF - Journal JO - JOR SP - 179 EP - 184 VL - 8 IS - 4 SN - 2147-8228-2147-8228 M3 - doi: 10.18100/ijamec.800905 UR - https://doi.org/10.18100/ijamec.800905 Y2 - 2020 ER -
EndNote %0 International Journal of Applied Mathematics Electronics and Computers Extractive Text Summarization System for News Texts %A Fahrettin Horasan , Burhan Bilen %T Extractive Text Summarization System for News Texts %D 2020 %J International Journal of Applied Mathematics Electronics and Computers %P 2147-8228-2147-8228 %V 8 %N 4 %R doi: 10.18100/ijamec.800905 %U 10.18100/ijamec.800905
ISNAD Horasan, Fahrettin , Bilen, Burhan . "Extractive Text Summarization System for News Texts". International Journal of Applied Mathematics Electronics and Computers 8 / 4 (Aralık 2020): 179-184 . https://doi.org/10.18100/ijamec.800905
AMA Horasan F. , Bilen B. Extractive Text Summarization System for News Texts. International Journal of Applied Mathematics Electronics and Computers. 2020; 8(4): 179-184.
Vancouver Horasan F. , Bilen B. Extractive Text Summarization System for News Texts. International Journal of Applied Mathematics Electronics and Computers. 2020; 8(4): 179-184.
IEEE F. Horasan ve B. Bilen , "Extractive Text Summarization System for News Texts", International Journal of Applied Mathematics Electronics and Computers, c. 8, sayı. 4, ss. 179-184, Ara. 2021, doi:10.18100/ijamec.800905

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