Published Paper Details:

Text summarization tools play a pivotal role in the era of information overload, aiding users in extracting the most pertinent information from voluminous texts. This abstract provides a succinct overview of text summarization tools, highlighting their significance, techniques employed, and potential applications .Text summarization tools employ various techniques, such as Text Preprocessing-( Tokenization, Lowercasing), Text Representation sequence to sequence models, Evaluation metrics, Hyper parameter Tuning ,extractive and abstractive summarization, to condense lengthy documents into shorter, coherent versions. Extractive summarization selects salient sentences or phrases from the original text, while abstractive summarization generates concise summaries by interpreting and rephrasing content .These tools rely on natural language processing (NLP), machine learning algorithms, and deep learning models to achieve these tasks. We also use tokenization ML algorithm to get quality of summary. It also have additional feature of uploading a pdf or file and getting its summary.