A systematic literature review of deep learning-based text summarization: Techniques, input representation, training strategies, mechanisms, datasets, evaluation, and challenges

Automatic Text Summarization (ATS) involves estimating the salience of information and creating coherent summaries that include all relevant and important information from the original text. Extensive research has been carried out on ATS since 1958, gradually evolving from simple to advanced techniques, including machine learning-based, neural network-based, and deep learning-based techniques. Progress has been made in both extractive and abstractive methods throughout this development. Despite numerous surveys on ATS, there remains a notable absence of a comprehensive literature review encompassing the latest advancements in deep learning techniques for text summarization. Therefore, this paper provides a Systematic Literature Review (SLR) of deep learning-based text summarization in both types (extractive and abstractive) between 2014 and 2023. To the best of our knowledge, this is the first SLR that offers a comprehensive overview of extractive and abstractive text summarization techniques based on Deep Learning models. According to the defined inclusion and exclusion criteria, 73 deep learning-based text summarization studies are selected for further investigation. The structure of the review is organized as follows. Firstly, it identifies and examines the deep learning models employed in both extractive and abstractive text summarization. Then, the input text's representation methods are identified and discussed clearly. Next, training strategies used in supervised extractive summarization techniques are identified. Furthermore, mechanisms that improve the abstractive summarization process are identified. Additionally, the most commonly used datasets and their advantages and disadvantages are discussed. The most commonly used evaluation metrics are also identified. Finally, the challenges and possible solutions to guide future research in the field are discussed.