Abstract

Text Summarization was proved to be an advantage over manually summarizing the large data. It condenses the salient features from the text by preserving the content and serves the meaningful summary. Classification can be done in two ways – extractive and abstractive summarization. Extractive summarization uses statistical and linguistic features to determine the important features and fuse them into a shorter version. Whereas abstractive summarization understands the whole document and then generates the summary. In this paper extractive and abstractive methods are framed.

References

• M. Haque, et al. , "Literature Review of Automatic Multiple Documents Text Summarization," International Journal of Innovation and Applied Studies, vol. 3, pp. 121-129, 2013.
• Weiguo Fan, Linda Wallace, Stephanie Rich, and Zhongju Zhang, "Tapping into the Power of Text Mining", Journal of ACM, Blacksburg, 2005.
• D. Das and A. F. Martins, "A survey on automatic text summarization," Literature Survey for the Language and Statistics II course at CMU, vol. 4, pp. 192-195, 2007.
• Farshad Kyoomarsi, Hamid Khosravi, Esfandiar Eslami and Pooya Khosravyan Dehkordy, "Optimizing Text Summarization Based on Fuzzy Logic", In proceedings of Seventh IEEE/ACIS International Conference on Computer and Information Science, IEEE, University of Shahid Bahonar Kerman, UK, 347-352, 2008.
• Vishal Gupta, G. Sl Lehal, "A Survey of Text Mining Techniques and Applications", Journal of Emerging Technologies in Web Intelligence, VOL. 1, NO. 1, 60-76, AUGUST 2009.
• Gupta V. and Lehal G. S. , "A Survey of Text Summarization Extractive Techniques", JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 2, NO. 3, AUGUST 2010
• Jimmy Lin. , "Summarization. ", Encyclopedia of Database Systems. Heidelberg, Germany: Springer-Verlag, 2009.
• Jackie CK Cheung, "Comparing Abstractive andExtractive Summarization of Evaluative Text: Controversiality and Content Selection", B. Sc. (Hons. ) Thesis in the Department of Computer Science of the Faculty of Science, University of British Columbia, 2008.
• Ani Nenkova and ,Rebecca Passonneau, "Evaluating content selction in summarization: The Pyramid method", in HLT-NAACL, 145-152, 2004.
• Chin-yew Lin, "A package for automatic evaluation of summaries",in Proc. ACL workshop on text summarization branches out,2004.
• Eduard Hovy, Chin-Yew Lin, Liang Zhou, and Junichi Fukumoto, "Automated Summarization Evaluation with Basic Elements", In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC), 2006.
• Kathleen Mackeown, Ani Nenkova, David Elson, Rebecca Passonneau, and Julia Hirschberg "A task based evaluation of multidocument system",in SIGIR'05, ACM, 2005.
• Madhavi K. Ganapathiraju, "Overview of summarization methods", 11-742: Self-paced lab in Information Retrieval, November 26, 2002.
• Klaus Zechner, "A Literature Survey on Information Extraction and Text Summarization", Computational Linguistics Program, Carnegie Mellon University, April 14, 1997
• Berry Michael W. , "Automatic Discovery of Similar Words", in "Survey of Text Mining: Clustering, Classification and Retrieval", Springer Verlag, New York, LLC, 24-43, 2004.
• Rene Arnulfo Garcia-Herandez and Yulia Ledeneva, "Word Sequence Models for Single Text Summarization", IEEE,44-48, 2009.
• Yongzheng, Nur and Evangelos, "Narrative Text Classification for Automatic Key Phrase Extraction in Web Document Corpora", WIDM'5, 51-57, Bremen Germany,2005.
• Canasai Kruengkari and Chuleerat Jaruskulchai, "Generic Text Summarization Using Local and Global Properties of Sentences", Proceedings of the IEEE/WIC international Conference on Web Intelligence (WI'03) , 2003.
• Joel larocca Neto, Alex A. Freitas and Celso A. A. Kaestner, "Automatic Text Summarization using a Machine Learning Approach", Book: Advances in Artificial Intelligence: Lecture Notes in computer science, Springer Berlin / Heidelberg, Vol 2507/2002, 205-215, 2002.
• Khosrow Kaikhah, "Automatic Text Summarization with Neural Networks", in Proceedings of second international Conference on intelligent systems, IEEE, 40-44, Texas, USA, June 2004.
• Khosrow Kaikhah "Text Summarization using Neural Networks", Department of Faculty Publications- Computer Science, Texas State University, eCommons,2004.
• Ladda Suanmali, Mohammed Salem, Binwahlan and Naomie Salim, "Sentence Features Fusion for Text summarization using Fuzzy Logic, IEEE, 142-145, 2009
• F. Canan Pembe and Tunga Güngör, "Automated Querybiased and Structure-preserving Text Summarization on Web Documents", Proceedings of the International Symposium on Innovations in Intelligent Systems and Applications, ?stanbul, June 2007.
• P. E. Genest and G. Lapalme, "Framework for abstractive summarization using textto- text generation," in Proceedings of the Workshop on Monolingual Text-To-Text Generation, 2011, pp. 64-73.
• Khan A. and Salim N. , "A REVIEW ON ABSTACTIVE SUMMARIZATION METHODS "Journal of Theoretical and Applied Information Technology 10th January 2014. Vol. 59 No. 1
• R. Barzilay, et al. , "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.
• R. Barzilay and K. R. McKeown, "Sentence fusion for multidocument news summarization," Computational Linguistics, vol. 31, pp. 297-328, 2005.
• C. -S. Lee, et al. , "A fuzzy ontology and its application to news summarization," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 35, pp. 859-880, 2005.
• H. Tanaka, et al. , "Syntax-driven sentence revision for broadcast news summarization," in Proceedings of the 2009 Workshop on Language Generation and Summarisation, 2009, pp. 39-47.
• P. -E. Genest and G. Lapalme, "Fully abstractive approach to guided summarization," in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers- Volume 2, 2012, pp. 354-358.
• H. Saggion and G. Lapalme, "Generating indicative-informative summaries with sumUM," Computational Linguistics, vol. 28, pp. 497-526, 2002.
• C. F. Greenbacker, "Towards a framework for abstractive summarization of multimodal documents," ACL HLT 2011, p. 75, 2011.
• F. Moawad and M. Aref, "Semantic graph reduction approach for abstractive Text Summarization," in Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on,2012, pp. 132-138.