Heuristic Initialization And Similarity Integration Based Model for Improving Extractive Multi-Document Summarization

Currently, the prominence of automatic multi document summarization task belongs to the information rapid increasing on the Internet. Automatic document summarization technology is progressing and may offer a solution to the problem of information overload. Automatic text summarization system has the challenge of producing high quality summary. In this paper, the design of generic text summarization model based on sentence extraction has been redirected into more semantic measure reflecting the two significant objectives: content coverage and diversity when generating summaries from multiple documents as an explicit optimization model. The proposed two models have been then coupled and defined as single-objective optimization problem. Also, different integrations of similarity measures have been introduced and applied to the proposed model in addition to the single similarity measure that bases on using Cosine, Dice and Jaccard similarity measures for measuring text similarity involving integrating double similarity measures and triple similarity measures. The proposed optimization model has been solved using Genetic Algorithm. Moreover, heuristic initialization has been proposed and injected into the adopted evolutionary algorithm to harness its strength. Document sets supplied by Document Understanding Conference 2002 (DUC2002) have been used for the proposed system as an evaluation dataset and as an evaluation metric, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) toolkit has been used for performance evaluation of the proposed method and for performance comparison against other baseline systems. Comparison results for the proposed optimization based model against other baselines verified that the proposed system outperforms other baseline approaches in terms of Rouge - 2 and Rouge -1 scores wherein it has recorded a score of 0.4542 for Rouge - 1 and 0.1623 for Rouge - 2.