A Survey of Advances in Text Summarization Methods

Information overload is one of the most urgent problems due to the Internet's explosive expansion. Because there is a wealth of information available on the Internet on any subject, many people will benefit from having the pertinent information condensed into a summary. It is quite difficult for people to manually summarize large volumes of text. As a result, there is now a greater need for elaborate and potent summarizers. Researchers have been striving to develop ways to construct summaries since the 1960s so that the summary generated by the machine must match that of the human-created summary. Text summarizing principles, including methodologies, procedures, standard datasets, evaluation metrics, and future research goals, are thoroughly and up-to-date analyzed in this paper. The two most widely used methods-extractive and abstractive-are covered in-depth in this study. Evaluating the summary and boosting the development of reused assets and structures facilitates comparing and duplicating findings and introducing competition to enhance outcomes. This study also covers various ways of evaluating generated summaries. Ultimately, this study concludes with several issues and directions for future research in the field of text summarizing that might be helpful to scholars in the future.