document: News article extraction with template-independent wrapper We consider the problem of template-independent news extraction . The state-of-the-art news extraction method is based on template-level wrapper induction , which has two serious limitations . 1 ) It can not correctly extract pages belonging to an unseen template until the wrapper for that template has been generated . 2 ) It is costly to maintain up-to-date wrappers for hundreds of websites , because any change of a template may lead to the invalidation of the corresponding wrapper . In this paper we formalize news extraction as a machine learning problem and learn a template-independent wrapper using a very small number of labeled news pages from a single site . Novel features dedicated to news titles and bodies are developed respectively . Correlations between the news title and the news body are exploited . Our template-independent wrapper can extract news pages from different sites regardless of templates . In experiments , a wrapper is learned from 40 pages from a single news site . It achieved 98.1 % accuracy over 3,973 news pages from 12 news sites .
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