Text data from a number of sources has grown rapidly in recent years. Automated text summarising creates a concise summary of the original content that incorporates all essential information. In the last few years, a massive amount of text data from a wide range of sources has been coming out. In order to be helpful, this vast amount of information and expertise must be adequately summarised. As a result, this study makes a double contribution. There are several strategies for extracting text summarization that are discussed in this work. Even more challenging are extraction issues for single and multiple document summarization. Textual evaluation and textual similarity measurement issues are at the heart of this project. Every text summarising situation can benefit from addressing the issues raised here. Extractive summarization strategies that address the highlighted issues are then reviewed.