Text summarization will be a very important function in next generation e-business-intelligence-service. While human beings have proven to be extremely capable summarizers, computer based automated abstracting and summarizing has proven to be extremely challenging tasks. The dominant approach to text summarization is selection-based, by which the most content-bearing sentences or passages are identified and selected to compose a summary. However, the results from such a process often suffer from flaws such as incoherent content and poor readability due to unclear relationships between the selected text excerpts, dangling references, and so on. In this paper we present a process model of topic guided text summarization - TIDE. By directing the content selection process with identified topic structure of the text, the logical relation between the selected sentences is captured and better presented, which will also be valuable resource for a more complete and precise understanding of text meaning in the next stage of processing.