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Abstract: Digital knowledge has become a crucial side of machine learning and is gift in large volumes on the web. To use this knowledge with efficiency, knowledge handling and process techniques are needed to filter data from documents and store them. Associate degree application of linguistic communication process, that helps in handling volumes of knowledge, is text summarization. Text summarization helps in condensation documents, and extract the necessary facts described in it. There are 2 techniques in text summarization: theoretical and extractive summarization. Extractive summarization extracts keywords from the document and combines them to supply a semantically incorrect outline, whereas, theoretical summarization produces a semantically correct outline of the text. During this paper, we have a tendency to compare completely different techniques to spot low and radio frequency words. We have a tendency to measure the techniques supported the right identification of positive and negative words.