Abstract
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 a high quality summary. In this study, the design of generic text summarization model based on sentence extraction has been redirected into a more semantic measure reflecting individually 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 a single-objective optimization problem. Also, for improving the performance of the proposed model, different integrations concerning two similarity measures have been introduced and applied to the proposed model along with the single similarity measures that are based on using Cosine, Dice and similarity measures for measuring text similarity. For solving the proposed model, Genetic Algorithm (GA) has been used. Document sets supplied by Document Understanding Conference 2002 ( ) have been used for the proposed system as an evaluation dataset. Also, as an evaluation metric, Recall-Oriented Understudy for Gisting Evaluation ( ) toolkit has been used for performance evaluation of the proposed method. Experimental results have illustrated the positive impact of measuring text similarity using double integration of similarity measures against single similarity measure when applied to the proposed model wherein the best performance in terms of and has been recorded for the integration of Cosine similarity and similarity.
Highlights
One of the most important challenges facing humans today is the rapid increase in the amount of data generated by users, especially those on the Internet
The main contribution of this paper is to model the multi-document text summarization task as an optimization problem
The proposed method was able to obtain high accuracy and improve the performance compared with the current techniques
Summary
One of the most important challenges facing humans today is the rapid increase in the amount of data generated by users, especially those on the Internet. For solving the discrete optimization problem in their work, they created an adaptive Differential Evolution algorithm They implemented their model on the task of multi-document summarization. ALGULIEV et al (2011) proposed an unsupervised model for text summarization which performs generation to a summary by means of an extraction to the significant sentences in given document(s). They modeled TS as an integer linear programming problem. In ALGULIEV et al (2013), a model based on optimization for generic text summarization has been proposed Their proposed model generated a summary through performing an extraction of significant sentences from documents. The optimization problem was solved through developing an adaptive differential evolution algorithm with a new mutation approach [10]
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