CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

Trends in Multi-document Summarization System Methods

by Abimbola Soriyan, Theresa Omodunbi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 16
Year of Publication: 2014
Authors: Abimbola Soriyan, Theresa Omodunbi
10.5120/17095-7804

Abimbola Soriyan, Theresa Omodunbi . Trends in Multi-document Summarization System Methods. International Journal of Computer Applications. 97, 16 ( July 2014), 46-52. DOI=10.5120/17095-7804

@article{ 10.5120/17095-7804,
author = { Abimbola Soriyan, Theresa Omodunbi },
title = { Trends in Multi-document Summarization System Methods },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 16 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number16/17095-7804/ },
doi = { 10.5120/17095-7804 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:24:19.689079+05:30
%A Abimbola Soriyan
%A Theresa Omodunbi
%T Trends in Multi-document Summarization System Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 16
%P 46-52
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Information is knowledge if it is rightly applied. Information are stored with different formats in databases but retrieving such from different documents has been a challenge. People want ready-made information for the purpose of decision making in minimal time and thereby crave for summary of information. Automatic summarization helps in mining data and delivering timely and cogent information to users. These systems attempt to address the issue of data mining using different summarization methods. This paper discusses existing methods and state of the art in automatic summarisation system from recent articles. Achievement and challenges involve are also discussed.

References
  1. Erkan, G. , and Radev, D. R. "LexRank: Graph-based lexical centrality as salience in text summarization", Journal of Artificial Intelligent Research. (JAIR), . 22(1), (2004), 457-479.
  2. Luhn, H. P. , "The Automatic Creation of Literature Abstracts", IBM Journal of Research and Development, 2(2), (1958), 159-165.
  3. Armbrust, M. , Fox, A. , Griffith, R. , Joseph, A. D. , Katz, R. H. , Konwinski, A. , Lee, G. , Pattersom, D. A. , Rabkin, A. , Stoica, I. , and Zaharia, M. 2009 Above the Clouds: A Berkeley View of Cloud Computing.
  4. Malathy, G. , Somasundaram, Rm and Duraiswamy K. , "Performance Improvement in Cloud Computing Using Resource Clustering", Journal of Computer Science 9 (6) (2013). 671-677, ISSN: 1549-3636
  5. Buyya, R. , Yeo, C. S. , Venugopal, S. , Broberg, J. , and Brandic, I. "Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility". Future Generation computer systems, 25(6), (2009), 599-616.
  6. Watson, H. J. , Goodhue, D. L. and Wixom, B. H. , "The benefits of data warehousing: Why some organizations realize exceptional payoffs". Information and Management, 39(6), (2002), . 491-502.
  7. Awoyelu, I. , Omodunbi, T. and Udo, J. "Bridging the Gap in Modern Computing Infrastructures: Issues and Challenges of Data Warehousing and Cloud Computing". Computer and Information Science, 7(1) (2013), 33.
  8. Krishnan, K. , 2013. Data Warehousing in the Age of Big Data, Elsevier.
  9. Pembe, F. , and Gungor, T. 2008. Towards a new summarization approach for search engine results: An application for Turkish. Proceedings of the 23rd International Symposium on Computer and Information Sciences, Istanbul, pp. 1-6
  10. Hahn, U. & Mani, I. , 2000. The challenges of automatic summarization. Computer, 33(11).
  11. Goldstein, J. , Mittal, V. , Carbonell, J. , and Kantrowitz, M. (2000, April). Multi-document summarization by sentence extraction. In Proceedings of the 2000 NAACL-ANLP Workshop on Automatic summarization-Volume 4 (pp. 40-48). Association for Computational Linguistics.
  12. Barzilay, R. , and McKeown, K. R. Sentence fusion for multi-document news summarization. Computational Linguistics, 31, (2005), 297–328.
  13. Nenkova, A. and McKeown, K. , A Survey of Text Summarization Techniques. In C. C. Aggarwal & C. Zhai, eds. Mining Text Data. Springer US, (2012) 43-76.
  14. West, D. B. 2001. Introduction to graph theory (Vol. 2). Upper Saddle River: Prentice hall.
  15. Mihalcea, R. 2004. Graph-based ranking algorithms for sentence extraction, applied to text summarization. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (p. 20). Association for Computational Linguistics.
  16. Mihalcea, R. and Tarau, P. , 2004. TextRank: Bringing order into texts. . Proceedings of EMNLP, 4(4), . 404–411.
  17. Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7).
  18. Theresa Omodunbi and Abimbola Soriyan (2012): Multi-Web page Summarization and Presentation using Pair-wise Bipartite Graph. Proceedings of 4th Annual International Conference on ICT for Africa, Kampala, March 21st-24th, pp. 241 - 242, Uganda.
  19. Zhang, Z. , Ge, S. S. , and He, H. (2012). Mutual-reinforcement document summarization using embedded graph based sentence clustering for storytelling. Information Processing & Management, 48(4), 767-778.
  20. Jimeno-Yepes, A. J. , Plaza, L. , Mork, J. G. , Aronson, A. R. , and Díaz, A. MeSH indexing based on automatically generated summaries. BMC bioinformatics, 14(1), (2013), 208
  21. Zajic, D. , Dorr, B. J. , Lin, J. and Schwartz, R. Multi-candidate reduction: Sentence compression as a tool for document summarization tasks . Information Processing and Management 43, (2007), 1549-1570.
  22. Yousfi-Monod, M. and Prince, V. 2008. Sentence Compression as a Step in Summarization or an Alternative Path in Text Shortening. CoLing: Companion volume: Posters 139-142.
  23. Dang, H. and Owczarzak, K. 2008. Overview of the TAC 2008 Update Summarization Task. In: Proceedings of the Text Analysis Conference, TAC 2008, Gaithersburg
  24. Dang, H. T. : Overview of DUC 2006. In: Proceedings of the HLT-NAACL 2006 Document Understanding Workshop.
  25. Salakoski, T. , Ginter, F. , Pyysalo, S. and Pahikkala T. (Eds. ): Advances in Natural Language Processing, 5th International Conference on NLP, FinTAL 2006, Turku, Finland, August 23-25, 2006.
  26. Gagnon, M. and Da Sylva, L. , 2006. Text compression by syntactic pruning. In ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS. pp. 312-323.
  27. Lin, J. , & Wilbur, W. J. Syntactic sentence compression in the biomedical domain: facilitating access to related articles. Information Retrieval, 10(4-5), (2007), 393-414.
  28. Chali, Y. & Sadid, H. 2012. On the Effectiveness of Using Sentence Compression Models for Query-Focused Multi-Document Summarization. Proceedings of COLING 2012, (December 2012), p. 457-474.
  29. Perera, P. and Kosseim, L. 2013. Evaluating Syntactic Sentence Compression for Text Summarisation. In Natural Language Processing and Information Systems (pp. 126-139). Springer Berlin Heidelberg.
  30. Marneffe, M. C. D. and Manning, C. D. 2008. The Stanford typed dependencies representation. In: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, CrossParser 2008, Manchester, pp. 1–8
  31. Jing, H. , 2000. Sentence reduction for automatic text summarization. ANLP, p. 310-315.
  32. Lin, C. Y. , and Hovy, E. 2000. The automated acquisition of topic signatures for text summarization. In Proceedings of the 18th conference on Computational linguistics-Volume 1 (pp. 495-501). Association for Computational Linguistics.
  33. Verma, R. , Chen, P. , and Lu, W. 2007. A Semantic Free-Text Summarization Systems Using Ontology Knowledge, Document Understanding Conference DUC 2007, pp. 1-5.
  34. Schuemie, M. J. , Kors, J. A. and Mons, B. 'Word Sense Disambiguation in the Biomedical domain: An Overview', Journal of Computational Biology, Vol. 12, No. 5, (2005), 554-565.
  35. Lyman, R. L. "Summary of Investigations Relating to Grammar, Language, and Composition (January, 1929, to January, 1931). II'. The Elementary School Journal, (1932), 352-363.
  36. Dang, (2014) CFP: NIST Biomedical Summarization shared task http://www. nist. gov/tac/2014/BiomedSumm/ Accessed 02/05/2014
  37. Daily Estimated size of World Wide Web. Retrieved from http://www. worldwidewebsize. com/ Accessed 14 June, 2014
  38. Jatowt, A. , and Ishizuka, M. 2004. Temporal web page summarization. In Web Information Systems–WISE 2004 (pp. 303-312). Springer Berlin
  39. Li, X. , Du, L. , & Shen, Y. D. (2013). Update summarization via graph-based sentence ranking. Knowledge and Data Engineering, IEEE
  40. He, R. , Qin, B. , and Liu, T. "A novel approach to update summarization using evolutionary manifold-ranking and spectral clustering. "Expert Systems with Applications 39. 3 (2012): 2375-2384
  41. Plaza, L. , Díaz, A. , and Gervás, P. A semantic graph-based approach to biomedical summarisation. Artificial intelligence in medicine, 53(1), (2011), 1-14.
  42. Yoo, I. , Hu, X. , and Song, I. Y. A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method. BMC bioinformatics, 8(Suppl 9), (2007), S4.
  43. Iroju, O. , Soriyan A. , Gambo I. , and Olaleke J. "Interoperability in Healthcare: Benefits, Challenges and Resolutions. " International Journal of Innovation and Applied Studies 3. 1 (2013), 262-270.
  44. Kavuluru R. , Han S. , and Harris D. (2013). Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques, NDLB 2013
  45. Scott, D. , Hallett, C. , & Fettiplace, R. Data-to-text summarisation of patient records: Using computer-generated summaries to access patient histories. Patient education and counseling, 92(2), (2013), 153-159.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining summarization information retrieval multi-document.