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Reseach Article

Domain Specific e-Document Summarization Using Extractive Approach

Published on None 2011 by Sunita R. Patil, Sunita M.Mahajan
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 13
None 2011
Authors: Sunita R. Patil, Sunita M.Mahajan
e73846de-3d9a-4c08-97cd-7be82fa43bb2

Sunita R. Patil, Sunita M.Mahajan . Domain Specific e-Document Summarization Using Extractive Approach. International Conference and Workshop on Emerging Trends in Technology. ICWET, 13 (None 2011), 36-41.

@article{
author = { Sunita R. Patil, Sunita M.Mahajan },
title = { Domain Specific e-Document Summarization Using Extractive Approach },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 13 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 36-41 },
numpages = 6,
url = { /proceedings/icwet/number13/2161-is81/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Sunita R. Patil
%A Sunita M.Mahajan
%T Domain Specific e-Document Summarization Using Extractive Approach
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 13
%P 36-41
%D 2011
%I International Journal of Computer Applications
Abstract

With the rapid growth of online information availability, it becomes more and more important to find and describe textual information effectively from multiple related e-documents. Domain specific related e-documents contain information which is much relevant, similar or repeated and shares same background. Reading these all multiple relevant e-documents completely for accurate & brief contents is time-consuming, unnecessary and impossible. In this scenario multidocument summarization is useful to give an outline of a topic from multiple related source documents and allow users to zoom in for more details as per interest.

References
  1. Sunita R. Patil.and Sunita M.Mahajan, A Novel Approach For Research Paper Abstracts Summarization Using Cluster Based Sentence Extraction, ACM-ICWET2011,TCET,Mumbai
  2. Shiyan Ou, Christopher S.G. Khoo and Dion H. Goh,Design and development of a concept-based multidocument summarization system for research abstracts. Published in Journal of Information Science OnlineFirst, on December 3, 2007 as doi:10.1177/0165551507084630.
  3. Dragomir R. Radev, Hongyan Jing, and Malgorzata Budzikowska, Centroid-Based Summarization of Multiple Documents: Sentence Extraction, Utility-Based Evaluation and User Studies. Information Processing and Management, Vol. 40, pp. 919–938, 2004.
  4. Endre Boros, Paul Kantor, and David J. Neu, A Clustering Based Approach to Creating Multi-Document Summaries, http://www.nlpir.nist.gov/projects/duc/pubs/2001papers/rutgers_final.pdf
  5. Patrick Pantel, and Dekang Lin, Document Clustering with Committees, Proceedings of ACM, SIGIR’02, New York: ACM, pp. 199–206, 2002.
  6. Po Hu, Tingting He, Donghong Ji, and Meng Wang, A Study of Chinese Text Summarization Using Adaptive Clustering of Paragraphs, Proceeding of the Fourth International Conference on Computer and Information Technology, Wuhan, pp. 1159–1164, 2004.
  7. Inderjeet Mani, Summarization Evaluation: An Overview, Proceedings of the NTCIR Workshop 2 Meeting on Evaluation of Chinese and Japanese Text Retrieval and Text Summarization, 2001.
  8. Daniel Marcu, From Discourse Structures to Text Summaries, ACL/EACL-97 Workshop on Intelligent Scalable Text Summarization, pp. 82–88, 1997.
  9. ChinYew Lin, and Eduard Hovy, Automatic Evaluation of Summaries Using N-gram Co-Occurrence Statistics, Proceedings of the Human Technology Conference (HLTNAACL-2003), Edmonton, Canada, 2003
  10. T. F. Hand, A proposal for task-based evaluation of text summarization systems, ACLEACL-97 summarization workshop, pp. 31–36, 1997.
  11. De-Xi Liu, Yan-Xiang He, Dong Hong Ji, Hua Yang, A Novel Chinese Multi-Document Summarization Using Clustering Based Sentence Extraction. Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 1-4244-0060-0/06/$20.00 ©2006 IEEE
  12. T. Joachims, A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization, Int.Conf. Machine Learning. 1997.
  13. Regina Barzilay and Michael Elhadad. Using lexical chains for text summarization. In Proceedings of the ACL/EACL Workshop on Intelligent Scalable Text Summarization, Madrid, Spain, 1997.
  14. Regina Barzilay and Lillian Lee. Catching the drift: Probabilistic content models, with applications to generation and summarization. In Proceedings of the 2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL 2004) , pages 113–120, Boston, Massachusetts, 2004.
  15. Regina Barzilay and Kathleen R. McKeown. Sentence fusion for multidocument news summarization. Computational Linguistics, 31(3):297–327, 2005.
  16. Harold P. Edmundson. New methods in automatic extracting. Journal of the ACM , 16(2):264–285, 1969.
  17. Jade Goldstein, Vibhu Mittal, Jaime Carbonell, and Jamie Callan. Creating and evaluating multi-document sentence extract summaries. In Proceedings of the Ninth International Conference on Information and Knowledge Management (CIKM 2000), pages 165–172, McLean, Virginia, 2000.
  18. Vasileios Hatzivassiloglou, Judith L. Klavans, and Eleazar Eskin. Detecting text similarity over short passages: Exploring linguistic feature combinations via machine learning. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-1999) , 1999.
  19. Kevin Knight and Daniel Marcu. Statistics-based summarization—step one: Sentence compression. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000) , pages 703–710, Austin, Texas, 2000.
  20. Hans Peter Luhn. The automatic creation of literature abstracts.IBM Journal of Research Development, 2(2):159–165, 1958.
  21. Inderjeet Mani, Barbara Gates, and Eric Bloedorn. Improving summaries by revising them. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL 1999) , pages 558–565, College Park, Maryland, 1999.
  22. Daniel Marcu. The Rhetorical Parsing, Summarization, and Generation of Natural Language Texts . PhD thesis, University of Toronto, 1997.
  23. Dragomir R. Radev, Sasha Blair-Goldensohn, and Zhu Zhang. Experiments in single and multi-document summarization using MEAD. In Proceedings of the 2001 Document Understanding Conference (DUC 2001) , 2001.
  24. David Zajic, Bonnie Dorr, Jimmy Lin, and Richard Schwartz. Multi-Candidate Reduction: Sentence compression as a tool for document summarization tasks. Information Processing and Management , 43(6):1549–1570, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Multi-document Summarization Clustering Extraction