Context Aware Model of Abstractive Text Summarization for Research Articles
Research article comprises of different sections each holds its own characteristic domain information. Summarization of entire article from multiple documents of multiple sections in precise form with special focus to contextual information is tedious. We proposed context aware model to extract contextual texts from research article by utilizing multi document directed graph for contextual matching phrases We customized extractive summarization for abstractive text summarization with lucid information as prime criterion. Decision matrix with elitism identification further fine tunes the abstractive text summary and outperforms at sentence level Rouge L measures 9.32 and summary level measures 89.65. Keywords1 Glowworm Swarm Optimization, Analytical Hierarchical Processing (AHP) Skip Gram Model, Word2vectors, Maximum Marginal Relevance, Log Likelihood Rate,N Gram.