A summary condenses a lengthy document by highlighting salient features. It helps reader to understand completely just by reading 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 that to select only pertinent information to include the article. The main goal of 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 on-line 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 done using ROUGE metric.

Text Summarization, Natural Language Processing, News Paper Articles, Intelligence mining, RDF Triplets ,NER

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