Description

Abstract

Summarization is the process of compressing the original document into a short summary by extracting the most important information from the document. Document summarization is an information retrieval task, which aims at extracting a condensed version of the original document. In this paper, we propose a context sensitive document indexing model based on the Bernoulli model of randomness. A new approach using the lexical association between terms to give a context sensitive weight to the document terms has been proposed. The proposed sentence similarity measure has been used with the baseline graph-based ranking models for sentence extraction. Using the proposed Bernoulli association measure, the lexical association between the terms in a target summary is higher compared to the association between the terms in a document. The concept of topical and nontopical terms was used to modify the indexing weights of the document terms. Analysis of some of the documents and the corresponding summary figured out the specific advantage offered by the proposed Bernoulli model-based context sensitive indexing.