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The process of text summarization is one of the applications of natural language processing that presents one of the most challenging obstacles. This is one of the most challenging duties since it demands an in-depth understanding of the information that is being retrieved from the text; as a result, it is one of the most time-consuming as well. Traditional methods of paraphrasing a text each come with their own individual set of restrictions; this is why it is vital to develop new methods in order to achieve better results in paraphrasing a text. Deep learning has been used, which has resulted in a paradigm shift in the way natural language processing is carried out. The tremendous progress that has been made in the fields of sentiment analysis, text translation, and text summarization can be attributed to the application of methodologies that are based on deep learning. The utilization of these various approaches, which resulted in the production of these advancements, is a primary cause of these breakthroughs. We have outlined a variety of deep learning procedures with the goals of summarizing texts and analyzing details in order to prepare these methods for possible applications in future research.
Muzffarali et al. (Fri,) studied this question.