A fast and robust method for web page template detection and removal

The widespread use of templates on the Web is considered harmful for two main reasons. Not only do they compromise the relevance judgment of many web IR and web mining methods such as clustering and classification, but they also negatively impact the performance and resource usage of tools that process web pages. In this paper we present a new method that efficiently and accurately removes templates found in collections of web pages. Our method works in two steps. First, the costly process of template detection is performed over a small set of sample pages. Then, the derived template is removed from the remaining pages in the collection. This leads to substantial performance gains when compared to previous approaches that combine template detection and removal. We show, through an experimental evaluation, that our approach is effective for identifying terms occurring in templates - obtaining F-measure values around 0.9, and that it also boosts the accuracy of web page clustering and classification methods.