document: 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 .
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