Development Of Text Characterization Using Natural Language Processing Techniques

Purpose:Text summarization means it reduces the text without losing the content. By this, we can reduce the text size. Method:In this work, we attempt to use tokenization and stop word removal to extract the useful and main content of the text. The model read the given text and performs tokenization, it considers the punctuations, and words as the token and it will eliminate the white spaces, then it removes the stop words. We calculate the cost of the sentence in the authentic corpus. By this, we can obtain our minimized text. Result:The proposed system can work with an accuracy of 96%, which means the summarized text is accurate to the given text.