Content Summary Generation Using NLP

Home / Data mining / Content Summary Generation Using NLP
Name

Content Summary Generation Using NLP

Technology DotNET,MS SQL
Category Web Application
Description
To find prominent summarized points in a collection of documents. We here propose a system to detect summarized points from a huge or multiple paragraph. We use an efficient method to discover summarized points from the provided content using Natural language processing (NLP). The provided content is divided into two parts as Summarized Content and Summarized Point. One would expect particular words to appear in the content more or less frequently: “dog” and “bone” will appear more often in documents about dogs, “cat” and “meow” will appear in documents about cats, and “the” and “is” will appear equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. Our proposed system captures this intuition in a mathematical framework and will examine the content of particular set of documents. Here the system will extract keywords and will use clustering algorithm in order to discover topic from particular set of documents. System will extract keywords which occur often and will cluster this keywords using clustering algorithm and will detect summarized point from a collection of documents. This system takes co-occurrence of terms into account which gives best result.
IEEE Paper Yes
IEEE Paper Year 2015
Previous
Next

Leave a Reply