In the view of a significant increase in the burden of information over and over the limit by the amount of information available on the internet, there is a huge increase in the amount of information overloading and redundancy contained in each document. Extracting important information in a summarized format would help a number of users. It is therefore necessary to have proper and properly prepared summaries. Subsequently, many research papers are proposed continuously to develop new approaches to automatically summarize the text. “Automatic Text Summarization” is a process to create a shorter version of the original text (one or more documents) which conveys information present in the documents. In general, the summary of the text can be categorized into two types: Extractive-based and Abstractive-based. Abstractive-based methods are very complicated as they need to address a huge-scale natural language. Therefore, research communities are focusing on extractive summaries, attempting to achieve more consistent, non-recurring and meaningful summaries. This review provides an elaborative survey of extractive text summarization techniques. Specifically, it focuses on unsupervised techniques, providing recent efforts and advances on them and list their strengths and weaknesses points in a comparative tabular manner. In addition, this review highlights efforts made in the evaluation techniques of the summaries and finally deduces some possible future trends.