Page-level template detection via isotonic smoothing

We develop a novel framework for the page-level template detection problem. Our framework is built on two main ideas. The first is theautomatic generation of training data for a classifier that, given apage, assigns a templateness score to every DOM node of the page. The second is the global smoothing of these per-node classifier scores bysolving a regularized isotonic regression problem; the latter follows from a simple yet powerful abstraction of templateness on a page. Our extensive experiments on human-labeled test data show that our approachdetects templates effectively.