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Extractive Multi-Document Summarization using Neural Network
Authors(2) :-Ravina Mohod, Prof. Vijaya Kamble
Natural language processing gives Text Summarization, which is the unmistakable application for information weight. Content diagram is a course of action of passing on a rundown by reducing the measure of outstanding document and relating essential information of champion report. There is rising a need to give grand chart in less time in light of the way that in exhibit time, the progress of data increments immensely on World Wide Web or on customer's work zones so Multi-Document once-completed is the best mechanical social affair to impact plot in less to time. This paper demonstrates an audit of existing techniques with the erraticismâ€™s including the need of sharp Multi-Document summarizer.
Ravina Mohod, Prof. Vijaya Kamble
Multi-Document Summarization; Clustering Based; Extractive and Abstractive approach; Ranked Based; LDA Based; Natural Language Processing
- 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.
- 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.
- Gupta, V. K., &Siddiqui, T. J. (2012, December). Multi-document summarization using sentence clustering.In
- Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on (pp. 1-5).IEEE.
- 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.
- 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.
- 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.
- Manjula.K.S “Extracting Summary from Documents Using K-Mean Clustering Algorithm” in IEEE IJARCCE, pp 3242-3246, 2013.
- Gawali, Madhuri, MrunalBewoor, and SuhasPatil. "Review: Evaluating and Analyzer to Developing Optimized Text Summary Algorithm."
- P.Sukumar, K.S.Gayathri “Semantic based Sentence Ordering Approach for Multi-Document Summarization” in IEEE IJRTE, pp 71-76, 2014.
- 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
- 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.
- Harshad Jain et. al. “Context Sensitive Text Summarization Using K Means Clustering Algorithm” IJSCE, pp no 301-304, 2012.
- García-Hernández, René Arnulfo, and YuliaLedeneva. "Word Sequence Models for Single Text Summarization."In Advances in Computer-Human
- 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.
- 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.
- 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.
- HongyanLill et al. “Multi-document Summarization based on Hierarchical Topic Model” HongyanLill, pp no 88-91.
- 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.
- Yan, Su, and Xiaojun Wan. "SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization."
Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 173-178
Manuscript Number : IJSRST184854
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Ravina Mohod, Prof. Vijaya Kamble, "Extractive Multi-Document Summarization using Neural Network", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.173-178, May-June-2018
URL : http://ijsrst.com/IJSRST184854