Deep Learning in Arabic Text Summarization: Approaches, Datasets, and Evaluation Metrics
Yasmin Einieh, Amal Abdullah AlMansour · 2022
Recently, there is a massive amount of data available on the internet. Hence, it is quite difficult for the users to go through all the available online information to generate a precise summary manually. Automatic Text Summarization (ATS) systems provide a solution to this problem as they produce a shorter and manageable version of the input text while keeping the most important information. Deep learning has achieved good results in Natural Language Processing (NLP) tasks and the use of deep learning techniques specifically in Automatic Text Summarization (ATS) has increased in English language. However, there is still a shortage of studies evaluating these techniques in Arabic language. In this research work, we review several articles that address the usage of deep learning with Arabic language. Specifically, we study the available models, datasets, and evaluation metrics for extractive and abstractive Arabic text summarization. We reviewed 12 research papers and found that most of the studies employed deep learning for the abstractive summarization type.