MCRMR: Maximum coverage and relevancy with minimal redundancy based multi-document summarization

In this paper, we propose a novel extraction based method for multi-document summarization that covers three important features of a good summary: coverage, non-redundancy, and relevancy. The coverage and non-redundancy features are modeled to generate a single document from the multiple documents. These features are explored by the weighted combination of word embedding and Google based similarity methods. To accommodate the relevancy feature in the system generated summaries, the text summarization task is modeled as an optimization problem, where various text features with their optimized weights are used to score the sentences to find the relevant sentences. For feature's weight optimization, we use the meta-heuristic approach, Shark Smell Optimization (SSO). The experiments are performed on six benchmark datasets (DUC04, DUC06, DUC07, TAC08, TAC11, and MultiLing13) with the co-selection and content based performance parameters. The experimental results show that the proposed approach is viable and effective for multi-document summarization. (C) 2018 Elsevier Ltd. All rights reserved.