Extracting Multi-Document Summary using Data Merging Technique

Authors

  • Vidhya Thakre  M. Tech Student, Department Computer Science & Engineering, G. H. Raisoni Institute of Engineering & Technology, Nagpur, Maharashtra, India
  • Prof. Antara Bhattcharya  Assistant Professor, Department of Computer Science & Engineering, G. H. Raisoni Institute of Engineering & Technology, Nagpur, Maharashtra, India

Keywords:

Multi-Document Summarization; Clustering Based; Extractive and Abstractive approach; Ranked Based; LDA Based; Natural Language Processing.

Abstract

Natural language processing gives Text Summarization, which is the unmistakable application for data weight. Content outline is an arrangement of passing on a summary by diminishing the measure of exceptional document and relating basic data of standout report. There is rising a need to give marvelous diagram in less time in light of the path that in present time, the headway of information increases hugely on World Wide Web or on client's work zones so Multi-Document once-finished is the best mechanical get together to influence outline in less to time. This paper shows a review of existing strategies with the eccentricities including the need of sharp Multi-Document summarizer.

References

  1. Van Britsom, Daan, Antoon Bronselaer, and Guy De Tre. "Using data merging techniques for generating multi-document summarizations." in IEEE trans. On fuzzy systems, pp 1 -17, 2013.
  2. Bagalkotkar, A., Kandelwal, A., Pandey, S., &Kamath, S. S. (2013, August). A Novel Technique for Efficient Text Document Summarization as a Service.InAdvances in Computing and Communications (ICACC), 2013 Third International Conference on (pp. 50-53). IEEE.
  3. Gupta, V. K., &Siddiqui, T. J. (2012, December). Multi-document summarization using sentence clustering.In
  4. Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on (pp. 1-5).IEEE.
  5. Ferreira, Rafael, Luciano de Souza Cabral, Rafael DueireLins, Gabriel Pereira e Silva, Fred Freitas, George DC Cavalcanti, Rinaldo Lima, Steven J. Simske, and Luciano Favaro. "Assessing sentence scoring techniques for extractive text summarization." Expert systems with applications 40, no. 14 (2013): 5755-5764.
  6. Guran, A., N. G. Bayazit, and E. Bekar. "Automatic summarization of Turkish documents using non-negative matrix factorization." In Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on, pp. 480-484.IEEE, 2011.
  7. ShashiShekhar “A WEBIR Crawling Framework for Retrieving Highly Relevant Web Documents: Evaluation Based on Rank Aggregation and Result Merging Algorithms” in Conf. on Computational Intelligence and Communication Systems, pp 83-88 ,2011.
  8. Manjula.K.S “Extracting Summary from Documents Using K-Mean Clustering Algorithm” in IEEE IJARCCE, pp 3242-3246, 2013.
  9. Gawali, Madhuri, MrunalBewoor, and SuhasPatil. "Review: Evaluating and Analyzer to Developing Optimized Text Summary Algorithm."
  10. P.Sukumar, K.S.Gayathri “Semantic based Sentence Ordering Approach for Multi-Document Summarization” in IEEE IJRTE, pp 71-76, 2014.
  11. JinqiangBian “Research On Multi-document Summarization Based On LDA Topic Model” in IEEE Conf. On Conference on Intelligent Human-Machine Systems and Cybernetics ,pp 113-116 , 2014
  12. Li, Yuhua, David McLean, Zuhair A. Bandar, James D. O'shea, and Keeley Crockett. "Sentence similarity based on semantic nets and corpus statistics."Knowledge and Data Engineering, IEEE Transactions on 18, no. 8 (2006): 1138-1150.
  13. Harshad Jain et. al. “Context Sensitive Text Summarization Using K Means Clustering Algorithm” IJSCE, pp no 301-304, 2012.
  14. García-Hernández, René Arnulfo, and YuliaLedeneva. "Word Sequence Models for Single Text Summarization."In Advances in Computer-Human
  15. Ferreira, R., de Souza Cabral, L., Lins, R. D., Pereira e Silva, G., Freitas, F., Cavalcanti, G. D., ...&Favaro, L. (2013). Assessing sentence scoring techniques for extractive text summarization. Expert systems with applications, 40(14), 5755-5764.
  16. Liu Na et al.“Mixture of Topic Model for Multi-document Summarization” In 2014 26th Chinese Control and Decision Conference (CCDC), IEEE, pp no 5168-5172.
  17. RachitArora et al. “Latent Dirichlet Allocation and Singular Value Decomposition based Multi-Document Summarization” In2008 Eighth IEEE International Conference on Data Mining, pp no 713-718.
  18. HongyanLill et al. “Multi-document Summarization based on Hierarchical Topic Model” HongyanLill, pp no 88-91.
  19. Liu, N., Tang, X. J., Lu, Y., Li, M. X., Wang, H. W., & Xiao, P. (2014, July). Topic-Sensitive Multi-document Summarization Algorithm. In Parallel Architectures, Algorithms and Programming (PAAP), 2014 Sixth International Symposium on (pp. 69-74). IEEE.
  20. Yan, Su, and Xiaojun Wan. "SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization."

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-04-30

Issue

Volume 4, Issue 2, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vidhya Thakre, Prof. Antara Bhattcharya, " Extracting Multi-Document Summary using Data Merging Technique, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 2, pp.375-381, March-April-2018.