Abstractive Web News Summarization Using Knowledge Graphs

The rapid progresses in digital data acquisition techniques have led huge volume of news data available in the news websites. Most of such digital news collections lack summaries. Due to that, online newspaper readers are overloaded with lengthy text documents. Also, it is a tedious task for human beings to generate an abstract for a news event manually since it requires a rigorous analysis of the news documents. An achievable solution to this problem is condensing the digital news collections and take out only the essence in the form of an automatically generated summary which allows readers to make effective decision in less time. The graph based algorithms for text summarization have been proven to be very successful over other methods for producing multi document summaries. The summary generated from knowledge graphs is more in line with human reading habits and possesses the logic of human reasoning. Due to the fast growing need of retrieving information in abstract form, we are proposing a novel approach for abstractive news summarization using the knowledge graphs to fulfill the need of having more accurate automatic abstractive text summarization.