Text summarization is the process of generating a high-quality summary through capturing vital details from long text documents. The conventional text summarization techniques possess difficulties in understanding the contextual information, as well as requiring excessive time. Therefore, deep learning and machine learning techniques have achieved a crucial progress in text summarization application. The research explores various techniques utilized in abstractive and extractive text summarization. Moreover, the study provides an overview of text summarization along with its limitations and advantages. Furthermore, the review discussed the various feature extraction and pre-processing mechanisms, which help to enhance the quality of the text. The research elaborated on the working of various transformer models used in text summarization. In addition, the study explores various datasets and diverse metrics utilized in text summarization. The significant research gained insights into various aspects of text summarization and set an outline for future research direction.