NewsIN: A News Summarizer and Analyzer

Abstract: A summary condenses a lengthy document by highlighting salient features. It helps the reader to understand completely just by reading a summary so that the reader can save time and also can decide whether to go through the entire document. Summaries should be shorter than the original article so make sure to select only pertinent information to include the article. The main goal of a newspaper article summary is, the readers to walk away with knowledge on what the newspaper article is all about without the need to read the entire article. This work proposes a news article summarization system which access information from various local online newspapers automatically and summarizes information using heterogeneous articles. To make ad-hoc keyword-based extraction of news articles, the system uses a tailor-made web crawler which crawls the websites for searching relevant articles. Computational Linguistic techniques mainly Triplet Extraction, Semantic Similarity calculation and OPTICS clustering with DBSCAN is used alongside a sentence selection heuristic to generate coherent and cogent summaries irrespective of the number of articles supplied to the engine. The performance evaluation is one using the ROUGE metric. The rapid progresses in digital data acquisition techniques have led to a 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 tothis problem is condensing the digital news collections and taking out only the essence in the form of an automatically generated summary which allows readers to make effective decisions 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 fromknowledge 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 news summarization and analyzer