The world wide web allows for diverse articles to be available on a news event, product or any topic. It is not impossible to find a few hundred articles that discuss a specific topic thus making it difficult for a user to quickly process the information. Summarization condenses huge volume of information related to a topic but does not provide a delineation of the issues pertaining to it. We want to extract the diverse issues pertaining to a topic by mining views from a collection of articles related to it. A view is a set of sentences, related in content, that address an issue relevant to a topic. We present a framework for extraction and ranking of views and have conducted experiments to evaluate the framework.