Abstractive Summarization with NLP Makes Easier to Memorize

Abstract

Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithm for abstractive summarization then on the basis of merits of various algorithm discussed in conclusions, we propose a new algorithm for abstractive summarization with the help of NLP to generate shortcuts from the content that make easier to memorize the content.