It is becoming hard to maintain web applications because of high complexity and duplication of web pages. However, most of research about code clone is focusing on code hunks, and their target is limited to a specific language. Thus, we propose GSIM, a language-independent statistical approach to detect similar pages based on scarcity and frequency of customized tokens. The tokens, which can be obtained from pages splitted by a set of given separators, are defined as atomic elements for calculating similarity between two pages. In this paper, the domain definition for web applications and algorithms for collecting tokens, making matrics, calculating similarity are given. We also conducted experiments on open source codes for evaluation, with our GSIM tool. The results show the applicability of the proposed method and the effects of parameters such as threshold, toughness, length of tokens, on their quality and performance.