Extractive Summarization for Arabic Documents Using SAraBERT with a Semantic Siamese Similarity Evaluation Metric

Abstract:In this research, we introduce SAraBERT, an enhanced version of AraBERT which proposes inter-sentence transformer layers for extractive summarization tasks. To ensure that the summaries generated by SAraBERT achieve a high coverage of the document's main ideas, we propose Semantic Siamese Similarity, a novel evaluation metric that measures the level of similarity between two text inputs. We validated using BLEU, ROUGE, and Semantic Siamese similarity on Sarabert and published related models. Simulation results showed the effectiveness of our proposed model and motivate follow on research.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.20964 [cs.CL]

(or arXiv:2608.20964v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.20964 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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