A Novel Gravity ‎Optimization Algorithm for Extractive Arabic Text Summarization

Abstract

An automatic text summarization system mimics how humans summarize by picking the most ‎significant sentences in a source text. However, the complexities of the Arabic language have become ‎challenging to obtain information quickly and effectively. The main disadvantage of the ‎traditional approaches is that they are strictly constrained (especially for the Arabic language) by the ‎accuracy of sentence feature ‎functions, weighting schemes, ‎and similarity calculations. On the other hand, the meta-heuristic search approaches have a feature that tolerates imprecision, gets ‎prohibited results, and is not strictly bound by the above ‎restrictions. This ‎paper used the Gravitational Optimization Algorithm (GOA), a powerful metaheuristic ‎approach ‎based on the law of ‎gravity, to address the challenge of extractive summarizing Arabic texts. The objective function of the GOA algorithm is derived based on sentence significance, such as its length, ‎similarity degree, position, statistical term frequency, and named entity ownership. Essex Arabic ‎Summaries Corpus (EASC) was used to evaluate the proposed method and measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The proposed approach achieved 68.04% Recall, ‎‎58.49% Precision, and 60.05% F1-measure using ROUGE-1, higher than standard summarizers and metaheuristic approaches

Keywords

• Abstractive Summarization, Extractive Summarization, Arabic Text Summarization, Similarity Graph, Gravitational Optimization Algorithm