  One of the most pressing issues that have arisen due to the rapid growth of
the Internet is known as information overloading. Simplifying the relevant
information in the form of a summary will assist many people because the
material on any topic is plentiful on the Internet. Manually summarising
massive amounts of text is quite challenging for humans. So, it has increased
the need for more complex and powerful summarizers. Researchers have been
trying to improve approaches for creating summaries since the 1950s, such that
the machine-generated summary matches the human-created summary. This study
provides a detailed state-of-the-art analysis of text summarization concepts
such as summarization approaches, techniques used, standard datasets,
evaluation metrics and future scopes for research. The most commonly accepted
approaches are extractive and abstractive, studied in detail in this work.
Evaluating the summary and increasing the development of reusable resources and
infrastructure aids in comparing and replicating findings, adding competition
to improve the outcomes. Different evaluation methods of generated summaries
are also discussed in this study. Finally, at the end of this study, several
challenges and research opportunities related to text summarization research
are mentioned that may be useful for potential researchers working in this
area.
