Abstractive vs Extractive Text Summarization (Output based approach) - A Comparative Study

There has been an explosion in the amount of digital data from a variety of sources, according to the IDC prediction, the total amount of digital data circulating annually around the world will hit 180 zettabytes in 2025 which pullulated from 4.4 zettabytes in 2013. These data are structured to semi-structured to fully unstructured. In today's world where machines are taking over everything the text summarization is nowhere behind it. various techniques are developed to extract the key points from text document to represent document summary. The paper briefly compares the two majorly used approaches (output based) for text summarization called extractive based text summarization and abstraction-based summarization, used in NLP for text summarization. The paper aims at reviewing the advantages, disadvantages and major features of both techniques using technique of TF-IDF Vectorisation along with word embedding vectors.