A Brief Study on Approaches for Extractive Summarization

Moved by the cutting edge mechanical advancements, information is to this century what oil was to the past one. Today, our reality is dropped by the gettogether and spread of gigantic measures of data. With a particularly enormous measure of information flowing in the advanced space, there is a need to create Artificial Intelligence calculations that can naturally abbreviate longer messages and convey exact outlines that can fluidly pass the proposed messages. This paper puts forth a brief survey of five major extractive methods of text summarization- the TFIDF, clustering, neural network, fuzzy logic, and graph-based approaches. A comparison of the five approaches is also presented.