Published Paper Details:

Text Summarization as a phenomenon has always been present and rather an evolving one with the advent of new technologies both in terms of data collection as well for the processing of this data. One reason of using text summarization is the huge amount of data floating over the internet in the form of text files, comments which is though potent enough to be used to extract useful information. but since the amount of text present in these sources is too huge, so the need of text summarization becomes justified by every argument. Some of the areas where text summarization is vastly used is applications involved in providing capsule information such as compact news applications, or websites providing academic notes for various examinations This paper presents an auto text summarizer application which takes the URL of a web page as input, performs summarization on the selected elements and then presents this summarized text content on the front end of a web application. At the backend, the process of scraping of web page content (if an http URL is provided as input) using beautiful soup library or reading of text provided takes place. news in short forms, or micro blogging websites. The scraped content after being preprocessed properly is summarized using a suitable library which in our case is one among NLTK, Spacy, Gensim and Sumy. The summarized content is presented at the front end using flask framework of Python. The results produced using different libraries are compared in the end in terms of reading time of the summarized content. The application uses extractive text summarization technique in order to achieve its result which is a compact summary of the textual data prepared