Extractive Summarization for Arabic Documents Using SAraBERT with a Semantic Siamese Similarity Evaluation Metric
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 21 Aug 2026.
Builds on
10 of 32 references.
References · 10
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
- 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 362
- 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
- 2019Fine-tune BERT for Extractive Summarizationcited by 122
- 2021The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Modelscited by 119
- 2019Leveraging BERT for Extractive Text Summarization on Lecturescited by 63
- 2016Classify or Select: Neural Architectures for Extractive Document Summarizationcited by 9
- 2014Multi-Topic Multi-Document Summarizercited by 4
Built on by
Not cited yet.