A novel hybrid system with hierarchical semantic conceptual dependency parsing for documents text summarization

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The rapid development of Internet technologies and the growing number of users who exchange a huge information in a different subject causes the emergence need to extract only the important and useful information from multiple documents. This extraction is text summarization which reduces the time in predicting the purpose of these documents and a mean to access effective and right decisions in important fields. The huge amount of information lead to the problem of memory overload. So, text summarization will minimize the effects of this problem. To overcome the problems of time consuming in extracting a proper information from multiple documents and reduce the redundant information which affect the processing overhead, a novel approach for summarizing information based on Hierarchical Conceptual Dependency of words and sentences representation presented in this paper. Moreover, concepts in text documents with score and token ranks are semantic networks representation of this text achieved for summarization. The proposed technique applied to documents d061j to d070f which are available to compare the results of any proposed approach in text summarization field with the experts output of summarization for the same documents indicated as a (reference summary). The proposed system was evaluated to check the accuracy of results and the tested dataset are Documents Understanding Conference (DUC 2002). Accuracy and efficiency of the output summarization evaluated using ROUGE–N and ROUGE-L metric. Hardware Resources used was Laptop Dell CORE "I3" with RAM 4 GB. Java software was the environment platform for this output. Comparing output results with the related algorithms depicted in this paper for the same target documents reflects that the similarity values between output summary via applying the proposed approach are more appropriate, accurate and minimum cost effective from the related metods.

     


  • Keywords


    Tokens; Active Concepts; ROUGE-N; Summary; Rank.

  • References


      [1] Abdi, A. et al., Information Processing and Management, Automatic Summarization assessment through a combination of semantic and syntactic information of intelligent educational systems, July 2015, Volume 51, Issue 4, P340-358.

      [2] Allahyari, P., ACM, Text Summarization Techniques: A Brief Survey, arXiv 28 July 2017.

      [3] Blanco, R., Matthews M, and Mika P, Information Processing and Management, Ranking of Daily

      [4] Codina-Filbà J, Bouayad-Agha J, Burga, Gerard Casamayor A, and Wanner L, Information Processing and Management, Using genre – specific features for patent summaries, January 2017, Volume 53 Issue Issue 1, pages 151- 174.

      [5] Hovy, E., "Text Summarization", Book: chapter 32, p584-595, 2000.

      [6] Lal, P., Project, Text Summarization, June 13, 2002.

      [7] Lloret, E., Text Summarization: An Overview, Alicante, the Spanish Government under the project TEXT-MESSS elloret@dlsi.ua.e,2008.

      [8] McBurney, W., "Automatic Source Code Summarization of Context for Java Methods", IEEE Transactions on Software Engineering, 2015.

      [9] Rossi, E., Centroid-based Text Summarization through Compositionality of Word Embedding" , Department of Computer Science University of Bari, 70125 Bari, Italy, Proceedings of the Multilingual 2017, Workshop on Summarization and Summary Evaluation Across Source Types and Genres, pages 12–21, Valencia, Spain, April 3, 2017.

      [10] Saleh, H., & Kadhim, Genetic Based Optimization Models for Enhancing Multi Document Text Summarization, Computer Science Department: University of Technology-Baghdad, 2015.

      [11] Sarraf, P., "Summarization of Document using Java|", International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 2, February – 2014.

      [12] Sankar, k., "An Approach to Text Summarization", AU-KBC Research Centre MIT Campus, Anna University MIT Campus, Anna University, Chennai- 44. Chennai- 44,sankar@au-kbc.org, Proceedings of CLIAWS3, Third International Cross Lingual Information Access Workshop, pages 53–60, Boulder, Colorado, June 2009.

      [13] Sood, A., “Towards Summarization of Written Text Conversations ", International Institute of Information Technology, June 2013.

      [14] Satyan, S., "Automatic Text Summarization", International Journal of IT, Engineering and Applied Sciences Research (IJIEASR) ISSN: 2319-4413 Volume 4, No. 4, April 2015.

      [15] Stefan., C. et. al, " On Definition of Automatic Text Summarization ", Proceedings of Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC2015), Dubai, UAE, 2015.

      [16] Sizov, G., " Extraction-Based Automatic Summarization" Norwegian University of Science and Technology" June 2010.

      [17] Shubham, A." Automatic Text Summarization", INDIAN INSTITUTE OF TECHNOLOGY MANDI JUNE, 2015.

      [18] YANG, G." Contextual Text Summarization for Content Processing in Mobile Learning ", Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences No 150, 2014.


 

View

Download

Article ID: 28366
 
DOI: 10.14419/ijet.v7i4.28366




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.