Wiki system, which allows users to collaboratively write articles online in a convenient way, has become popular in recent years. By design, wiki encourage a large number of users to make many edits rapidly. This characteristic makes it impractical to identify editor’s contributions of an article by examining each edit manually. However, finding major contributors are, in many cases, useful for wiki community members. Therefore, a method to automatically identify and summarize user contribution can be helpful. In this thesis, we proposed a framework to programmatically analyze wiki article edits, present it in a way that is close to human understanding, and measure the significance of the edit using a metric. With the significance value of each edit on hand, we can then perform different kinds of aggregation to find out who is the major contributor of an article, what topic writers are interested, what type of contribution are made by them, and many others.