LAUSR

Summary is the meaningful concise version of a text document. Generally existing statistical, knowledge based and discourse based extractive summarization methods use sentence similarity to extract informative sentences. This paper presents an innovative application of semigraph which includes the processes of semigraph construction and sentence extraction. Multilevel association among significant features of the text document can be represented using semigraph. Multi vertices property of semigraph helps in finding linear and nonlinear relationship between features. Some variation in semigraph in context of text document is proposed in this paper. The threshold for sentence length is calculated dynamically based on the sentence score. Challenge of measuring and analyzing performance is countered using proposed HIT ratio and ROUGE measures. Substantial experiments on benchmark dataset demonstrate that the proposed solution achieves encouraging performance. Multi directed mapping among summaries generated, using existing method is used to calculate effective index.