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Automatic Text Summarization (ATS) is the key challenge in the area of Natural Language Processing (NLP). It deals with generalizing a summary from a given text without losing the vital information. This is a contemporary area because of exponential content growth in internet and applied in summarizing the content available in books, newsletters, internal document analysis, patent research, e-learning etc. Various machine learning approaches are used in order to achieve the performance of human-generated summaries. The system fails to perform at few areas like checking grammatical errors and paraphrasing the sentences after the summary creation. This work provides a brief view on methods and approaches used in ATS.

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