Paper title:

Enhancements to graph based methods for multi document summarization

Published in: Issue 2, (Vol. 3) / 2009
Publishing date: 2009-10-20
Pages: 66-72
Author(s): Hariharan Shanmugasundaram, Srinivasan Rengaramanujam
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- PageRank type and PageRank type methods. Of the methods already proposed - the Centrality Degree method belongs to the former category while LexRank and Continuous LexRank methods belong to later category. The paper goes on to suggest two enhancements to both PageRank type and non- PageRank 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. In all 14 methods -six of non- PageRank type and eight of PageRank type have been investigated. 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 14 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

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