A Systematic Literature Review on Text Generation Using Deep Neural Network Models

• Published in: IEEE access 2022, Vol.10, p.53490-53503
• Main Authors: Fatima, Noureen, Imran, Ali Shariq, Kastrati, Zenun, Daudpota, Sher Muhammad, Soomro, Abdullah
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bibliographies; Computer and Information Sciences Computer Science; Data models; Data- och informationsvetenskap; Deep learning; Domains; GPT; Literature reviews; LSTM; Machine learning; Measurement; Motion pictures; natural langauge processing; natural language processing; neural network; Predictive models; quality metrics; Social networks; Systematic literature review; Systematics; text generation survey
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3174108

In recent years, significant progress has been made in text generation. The latest text generation models are revolutionizing the domain by generating human-like text. It has gained wide popularity recently in many domains like news, social networks, movie scriptwriting, and poetry composition, to name a few. The application of text generation in various fields has resulted in a lot of interest from the scientific community in this area. To the best of our knowledge, there is a lack of extensive review and an up-to-date body of knowledge of text generation deep learning models. Therefore, this survey aims to bring together all the relevant work in a systematic mapping study highlighting key contributions from various researchers over the years, focusing on the past, present, and future trends. In this work, we have identified 90 primary studies from 2015 to 2021 employing the PRISMA framework. We also identified research gaps that are further needed to be explored by the research community. In the end, we provide some future directions for researchers and guidelines for practitioners based on the findings of this review.