Aspect-based sentiment analysis (ABSA) is a method used to identify the aspects discussed in a given text and determine the sentiment expressed towards each aspect. This can help provide a more fine-grained understanding of the opinions expressed in the text. The majority of Arabic ABSA techniques in use today significantly rely on repeated pre-processing and feature-engineering operations, as well as the use of outside resources (e.g., lexicons). In essence, there is a significant research gap in NLP with regard to the use of transfer learning (TL) techniques and language models for aspect term extraction (ATE) and aspect polarity detection (APD) in Arabic text. While TL has proven to be an effective approach for a variety of NLP tasks in other languages, its use in the context of Arabic has been relatively under-explored. This paper aims to address this gap by presenting a TL-based approach for ATE and APD in Arabic, leveraging the knowledge and capabilities of previously trained language models. The Arabic base (Arabic version) of the BERT model serves as the foundation for the suggested models. Different BERT implementations are also contrasted. A reference ABSA dataset was used for the experiments (HAAD dataset). The experimental results demonstrate that our models surpass the baseline model and previously proposed approaches.

Large text documents are sometimes challenging to understand and time-consuming to extract vital information from. These issues are addressed by automatic text summarizing techniques, which condense lengthy texts while preserving their key information. Thus, the development of automatic summarization systems capable of fulfilling the ever-increasing demands of textual data becomes of utmost importance. It is even more vital with complex natural languages. This study explores five State-Of-The-Art (SOTA) Arabic deep Transformer-based Language Models (TLMs) in the task of text summarization by adapting various text summarization datasets dedicated to Arabic. A comparison against deep learning and machine learning-based baseline models has also been conducted. Experimental results reveal the superiority of TLMs, specifically the PEAGASUS family, against the baseline approaches, with an average F1-score of 90% on several benchmark datasets.