A tool to automatically summarize meaningful information from large collections of unstructured text would be very useful to researchers and analysts. Challenges include updating topic-focused summaries from vast amounts of text-based streaming data like newscasts, tweets, chats and blogs, that are noisy, short, and use non-standard language. Further, exploiting the time attribute associated with document summaries would help increase the speed and effectiveness of the analysis. In addition, the methodology developed must be compatible with existing text analytic and natural language processing techniques and tools. To address this, IAI will continue developing a novel Diversity rank-based Summarization Tool (DST) for extracting and updating succinct summary from streaming text. DST is a large-scale, dynamic and fast approach for maximizing the ability of intelligence analysts to analyze text-based communication to assess the ever-changing content and trend. With DST, the evolution of the target topic is tracked in real-time. The text network is constructed and updated very efficiently to enhance real-time summary updating. The importance, diversity and novelty of content in the summary are balanced and optimized through a principled graph-regularization framework. In addition, big data and cloud computing issues are addressed to improve the scalability of the text summarization task.