Automatic Abstractive Summarization Task for New Article

  • Afrida Helen
Keywords: Abstractive, Extractive, Statistic, Natural Processing Language, News Article.


Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured. This study discusses the tasks of generating summary. The conclusion is we can find that there are still opportunities to develop better outcomes that are better coherence and better accuracy.


Download data is not yet available.


Luhn, H.P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development. 

PE Genest, G Lapalme. (2011), Framework for Abstractive Summarization using text-to-text generation. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 64–73, Portland, Oregon, 24 June 2011. Association for Computational Linguistics (ACL)

Madanapalli, Andhra Pradesh, (2008), Knowledge Extraction Using Rule Based Decision Tree Approach. International Journal of Computer Scienceand Network Security IJCSNS, VOL.8 No.7, India.

Trevor Cohn and Mirella Lapata, (2008), Sentence Compression Beyond Word Deletion Proceedings of the 22nd International Conference on Computational Linguistics (Coling), pages 137–144 Manchester.

S. M. Harabagiu and F. Lacatusu, [2002], Generating single and multi-document summaries with gistexter," in Document Understanding Conferences (MUC)

R. Barzilay et al, [2003] ("Information fusion in 
the context of multi-document summarization," in Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, 1999, pp. 550- 557)

Jing, H., & McKeown, K. R. (2000). Cut and paste based text summarization. In Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference (pp. 178-185). Association for Computational Linguistics.

Hideki Tanaka, Akinori Kinoshita, Takeshi Kobayakawa, Tadashi Kumano, and Naoto Kato. (2009). Syntax- driven sentence revision for broadcast news summa- rization. In Proceedings of the 2009 Workshop on Lan- guage Generation and Summarisation, UCNLG+Sum ’09, pages 39–47, Stroudsburg, PA, USA. Association for Computational Linguistics.

Masayu Leylia Khodra, Dwi Hendratmo Widyantoro, E. Aminudin Aziz Bambang Riyanto Trilaksono, (2012), Automatic Tailored Multi Rhetorical Document Profile and Summary Specification, Journal of ICT Research and Applications. Published by ITB Journal Publisher, Vol. 6, No. 3, 2012, pp 220-239, ISSN: 1978-3086, DOI: 10.5614.

Afrida Helen, Ayu Purwarianti, Dwi Hendratmo Widyantoro (2014), Extraction and Classification of rhetorical sentences of experimental technical paper based on section class, 2nd International Conference on Information and Communication Technology (ICoICT), 2014, Date of Conference: 28-30 May 2014, IEEE Xplore: DOI: 10.1109/ICoICT.2014.6914099

Afrida Helen, Ayu Purwarianti, Dwi Hendratmo Widyantoro, (2015), Rhetorical Sentences Classification Based on Section Class and Title of Paper for Experimental Technical Papers, Journal of ICT Research and Applications. Published by ITB Journal Publisher, Vol. 9, No. 3, 2015, pp. 288-310, ISSN: 2337-5787, DOI: 10.5614/

How to Cite
Helen, A. (2018). Automatic Abstractive Summarization Task for New Article. EMITTER International Journal of Engineering Technology, 6(1), 22-34.