Automatic Text Summarization Review: Techniques, Applications, Trends, and Future Directions Through Systematic and Bibliometric Analysis

Automatic text summarization (ATS) is one of the applications of natural language processing (NLP) that is used to summarize large and complex documents as per the user’s convenience. ATS saves the time and efforts of the user by providing a concise summary of the lengthy text. There is a need to extensively explain this interesting field of ATS qualitatively as well as quantitatively. There are a limited amount of studies that performed systematic and bibliometrix analyses in the field of ATS. As a result, to fill this research gap, this article presents a bibliomeric analysis using 4,193 documents and a systematic analysis by studying 104 documents from literature. A systematic analysis identified superior approaches, evaluation methodologies, applications, challenges, future trends, prospects, and numerous strategies for meaningful summarization. The bibliometric analysis was performed using R and VOSviewer software on 4,193 documents from the Scopus database. This bibliometric analysis helps determine the latest trends, citations, authors, contributions, and collaborations of various authors, countries, and trendy keywords. It also identifies relationships between different entities such as keywords, authors, and countries. Furthermore, different output based extractive, abstractive, and hybrid summarization techniques are evaluated based on different criteria and elements to ascertain the optimal method for text summarization.