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Refereed Papers

Track: Data Mining

Paper Title:
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 the automatic generation of training data for a classifier that, given a page, assigns a templateness score to every DOM node of the page. The second is the global smoothing of these per-node classifier scores by solving 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 approach detects templates effectively.

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