Machine Learning based Extractive Text Summarization Techniques

Automatic Text Summarization (ATS) is a developing area of Natural Language Processing (NLP). It is concerned with simplifying a summary from a given text while retaining key information. Because of the exponential increase of content on the internet, this is a current area that is used in summarizing a text available in books, newsletters, internal document analysis, patent research, e-learning, and so on. Manually summarizing large amounts of text is difficult for humans. As a result, the demand for more complicated and powerful summarizers has skyrocketed. To match the performance of human-generated summaries, various machine learning algorithms are applied. Extractive text summarization finds and connects fundamental segments of a record to make a more limited report that sums up the first archive’s items. A machine learning approach for Extractive text summarization has been explored in this paper.