Total views : 114

A Graph Based Conceptual Mining Model for Abstractive Text Summarization


  • Dept of Computer Science & Applications, Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham, Amrita University, India
  • Dept of Mathematics, Amrita School of Engineering, Bangalore, Amrita Vishwa Vidyapeetham,Amrita University, India
  • Dept of Computer Science & Applications, Amrita School of Engineering, Amritapuri , Amrita Vishwa Vidyapeetham, Amrita University, India


Objectives: Themain objective of automatic text summarization is to compress the document into a smaller version by preserving the important concepts. Methods/Statistical Analysis:This work proposes a hybrid approach of Singular Value Decomposition and Named Entity Recognition to extract important sentences present in a document. The extracted sentences are used to create a probabilistic graphical model calledaBeliefcNetwork. This graph model represents documentsummary in concept level. We have used a modified Page Rank algorithm to find the most ranked noun phrase. From this noun phrase we extracted the most relevant sentences. Findings: Our abstractive graph based model for a document generates novel sentences as it uses the concept of triplets (Subject, Verb, and Object). It identifies whether a sentence is created by structural rearrangement of another sentence.Using SVD(Singualr Value Decomposition) and NER(Named Entity Recognition) we extracted relevant information present in a document so that entire document is crushed in to a graph model. We can use this model for documents similarity as well as for plagiarism detection. Application/Improvements.Experimental results of our proposed system show that use of named entities and SVD increases the accuracy of summarizer.


Abstractive Text Summarization, Named Entity Recognition, Page Ranking Algorithm, Singular Value Decomposition, Text Documents.

Full Text:

 |  (PDF views: 66)


  • Rabiner LR. A Tutorial on Hidden Markov model and selected applications in speech recognition. 2002.
  • Moawad IF, Aref M. Semantic graph reduction approach for abstractive Text Summarization. 7th International Conference on in Computer Engineering and Systems (ICCES).2012; p. 132–8.
  • Martin CD , Mason AP. Extraordinary SVD the mathematical association of america. The American Mathematical Monthly. 2012 December; 119(10):815–906.
  • C.-S. Lee. A fuzzy ontology and its application to news summarization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 2005; 35:859–80.
  • Vaishnavi V 1, Saritha M, Milton RS. Paraphrase Identification in Short Texts using Grammar Patterns.International Conference on Recent Trends in Information Technology (ICRTIT). 2013.
  • Barzilay K, McKeown KR, Elhadad M. Information Fusion in the Context of Multi-Document Summarization.Proceedings of the 37th annual meeting of the ACL. 1999 Jun 20.
  • Mihalcea R.Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization.Proceedings of the ACL 2004 on Interactive poster and demonstartion sessions.2004 July 21.
  • Duhan N, Sharma AK, Bhatia KK.Page Ranking Algorithms: A Survey. 2009 IEEE International Advance Computing Conference (IACC 2009). 2009 6-7 March.
  • Steinberger J, Karel Jeˇzek. Evaluation measures for text summarization,Computing and Informatics.2009; 28: 1001–26.
  • PageRank. 2016 January 1. Available from: https://
  • Anuradha, Deepak Kumar N.Characteristic Selection with Rough Sets for Web Page Ranking.Indian Journal of Science and Technology. 2016 Sep; 9(33): Doi no:10.17485/ ijst/2016/v9i33/97032.
  • JeongYon Shim.Dynamic Switching Belief Network based on IoT Knowledge Capsule.Indian Journal of Science and Technology. 2016 Sep; 9(35): Doi no: 10.17485/ijst/2016/ v9i35/101759.
  • Priyadharshini V, Malathi A.Analysis of Process Mining Model for Software Reliability Dataset using HMM. Indian Journal of Science and Technology. 2016 Jan. 9(4): Doi no:10.17485/ijst/2016/v9i4/52931.
  • Geetha Rani IS, Mageswari MS.A Link-click-concept Based Ranking Algorithm for Ranking Search Results.Indian Journal of Science and Technology. 2014 Jan: 7(10), Doi no:10.17485/ijst/2014/v7i10/50682.
  • Veena G, Lekha NK.An Extended Chameleon Algorithm for Document Clustering.Track Description: Volume in the prestigious Advances in Intelligent and Soft Computing (Springer)Series.3rd International Symposium on Intelligent Informatics (ISI’14). 2014 September 24-27.
  • Veena G, Lekha NK.A Concept based Clustering Model for Document Similarity. Proceedings of IEEE International Conference on Data Science and Engineering ICDSE 2014.Kerala, India. August 2014.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.