Leveraging Transformer Summarizer to Extract Sentences for Arabic Text Summarization

Automatic Text Summarization (ATS) is one of the fastest-growing areas of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). Automatic text summarizing aims to create summaries by extracting relevant information from input texts. Being able to study text non-sequentially allowed for large model training, making the Transformer the most widely used NLP model. This study focuses on extractive Arabic text summarization using various transformer-based summarization models. Two distinct scenarios are explored: summarization extracted from scored sentences and summarization generated from ranked (reversed) sentences. Experimental results demonstrate the effectiveness and performance of the transformer-based summarization models in the context of Arabic text summarization. The EASC corpus is used to evaluate our proposed summarization approach and ROUGE evaluation method is applied to determine the accuracy of the proposed approach. The findings of the study demonstrate notable performance when compared to other relevant works. The comparison between the two scenarios provides insights into the strengths and weaknesses of each approach. The scored sentence extraction scenario offers a more direct and content-focused summary generation, while the ranked sentence scenario provides flexibility in summarizing diverse text sources.