doi: 10.7763/IJET.2010.V2.107
Enhancements to Graph Based Methods for Single Document Summarization
- Department of Information Technology, B.S.Abdur Rahman University, Chennai, Tamilnadu, India
Abstract
This paper focuses its attention on extractive summarization using popular graph based approaches. Graph based methods can be broadly classified into two categories: non-Page Rank type and Page Rank type methods. Of the methods already proposed - the Centrality Degree method belongs to the former category while Lex Rank and Continuous Lex Rank methods belong to later category. The paper goes on to suggest two enhancements to both Page Rank type and non Page Rank type methods. The first modification is that of recursively discounting the selected sentences, i.e. if a sentence is selected it is removed from further consideration and the next sentence is selected based upon the contributions of the remaining sentences only. Next the paper suggests a method of incorporating position weight to these schemes. Thus we have experimented with 12 methods –six of non- Page Rank type and six of Page Rank type. To clearly distinguish between various schemes, we call the methods of incorporating discounting and position weight enhancements over Lexical Rank schemes as Sentence Rank (SR) methods. Intrinsic evaluation of all the 12 graph based methods were done using conventional Precision metric and metrics earlier proposed by us - Effectiveness1 (E1) and Effectiveness2 (E2). Experimental study brings out that the proposed SR methods are superior to all the other methods.
Keywords
- Page rank
- lexical rank
- sentence rank
- recommendation
- degree
- damping
- threshold
- effectiveness
- discounting
How to Cite
Shanmugasundaram Hariharan and Rengaramanujam Srinivasan, "Enhancements to Graph Based Methods for Single Document Summarization," International Journal of Engineering and Technology, vol. 2, no. 1, pp. 101-111, 2010. https://doi.org/ 10.7763/IJET.2010.V2.107
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).



