  In this era of the Internet, the amount of news articles added every minute
of everyday is humongous. As a result of this explosive amount of news
articles, news retrieval systems are required to process the news articles
frequently and intensively. The news retrieval systems that are in-use today
are not capable of coping up with these data-intensive computations. Cloudpress
2.0 presented here, is designed and implemented to be scalable, robust and
fault tolerant. It is designed in such a way that, all the processes involved
in news retrieval such as fetching, pre-processing, indexing, storing and
summarizing, exploit MapReduce paradigm and use the power of the Cloud
computing. It uses novel approaches for parallel processing, for storing the
news articles in a distributed database and for visualizing them as a 3D
visual. It uses Lucene-based indexing for efficient and faster retrieval. It
also includes a novel query expansion feature for searching the news articles.
Cloudpress 2.0 also allows on-the-fly, extractive summarization of news
articles based on the input query.
