Whenever we collect the main points from a different documents or from a big paragraphs, it becomes quite a tough and a tedious task to summarize it. Extractive text summarization is a technique which provides a brief summary extracting significant sentences and main features from a particular text, document or a paragraph. In this paper, a comparative study and a literature survey have been performed on extractive learning-based text summarization which includes classification of different algorithms, introducing techniques to compare between current models and traditional models and an analysis and observation based on these criteria that which is a best method. Text summarization using Natural Language Processing (NLP) is indeed a valuable tool for organizations dealing with vast amounts of customer feedback and data. It helps in extracting meaningful insights from large volumes of text, making it easier for people to understand semantic relationship behind that text. This survey mainly focusses on a wide range of study that includes feature representation to sentence selection and tells that how to generate a summary using machine learning models, graph methods, frequency driven approach, etc. The overall survey will definitely help us how to effectively and easily handle large sets of data in building an effective Natural Language Processing applications.