With the flourishing of various Web applications, the Internet has become one of the most important means to access news. According to one investigation, in the population of Internet users, 78.5% are looking for news. Unfortunately, although the Internet provides a platform for easily sharing information, it also brings a fast explosion of the news data. It leads to the fact that people spend more and more time to digest the data. Can we design new ways to help the users quickly understand and explore the news data? Information retrieval is one way, but it is insufficient. In this paper, we propose a flexible topic-driven framework, namely NewsInsight, for news exploration. This framework innovatively integrates a probabilistic topic model, graphical data analysis, and natural language processing. It performs news mining at the topic level and presents news information with topics, entities (e.g., people, organization, and events), and relations derived from the news data. Based on this framework, we have developed a system which can help people to understand and explore news from multiple dimensions. The trial operation of the system has worked at Xinhua News Agency, one of the biggest news publishers in China. Feedback from users shows that the system has achieved its primary objectives.