Implementation of Data Merging Technique for Multi-Document Summarizer

Authors(2) :-Poonam Bhagat, Prof. Roshni Talmale

Natural language processing gives Text Summarization, which is the unmistakable application for information weight. Content summary is a system of conveying a rundown by decreasing the measure of one of a kind document and relating imperative information of one of a kind report. There is rising a need to give awesome blueprint in less time in light of the way that in introduce time, the advancement of data augments colossally on World Wide Web or on customer's work areas so Multi-Document once-over is the best mechanical assembly to make summary in less time. This paper presents an audit of existing methods with the peculiarities featuring the need of keen Multi-Document summarizer.

Authors and Affiliations

Poonam Bhagat
M.Tech Student, Department Computer Science & Engineering, Tulsiramji Gaikwad Patil College of Engineering and Technology, Nagpur, Maharastra, India
Prof. Roshni Talmale
Assistant Professor, Department Computer Science & Engineering, Tulsiramji Gaikwad Patil College of Engineering and Technology, Nagpur, Maharashtra, India

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

  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."

Publication Details

Published in : Volume 3 | Issue 4 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 968-974
Manuscript Number : CSEIT11833447
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Poonam Bhagat, Prof. Roshni Talmale, "Implementation of Data Merging Technique for Multi-Document Summarizer", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.968-974, March-April-2018. |          | BibTeX | RIS | CSV

Article Preview