A Novel Gravity ‎Optimization Algorithm for Extractive Arabic Text Summarization

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Mustafa J. Hadi
Ayad R. Abbas
Osamah Y. Fadhil



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


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J. Hadi M, Abbas AR, Fadhil OY. A Novel Gravity ‎Optimization Algorithm for Extractive Arabic Text Summarization. Baghdad Sci.J [Internet]. 2024 Feb. 1 [cited 2024 Feb. 22];21(2):0537. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/7731


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