document: Can we learn a template-independent wrapper for news article extraction from a single training site ? Automatic news extraction from news pages is important in many Web applications such as news aggregation . However , the existing news extraction methods based on template-level wrapper induction have three serious limitations . First , the existing methods can not correctly extract pages belonging to an unseen template . Second , it is costly to maintain up-to-date wrappers for a large amount of news websites , because any change of a template may invalidate the corresponding wrapper . Last , the existing methods can merely extract unformatted plain texts , and thus are not user friendly . In this paper , we tackle the problem of template-independent Web news extraction in a user-friendly way . We formalize Web 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 . Correlations between news titles and news bodies are exploited . Our template-independent wrapper can extract news pages from different sites regardless of templates . Moreover , our approach can extract not only texts , but also images and animates within the news bodies and the extracted news articles are in the same visual style as in the original pages . In our experiments , a wrapper learned from 40 pages from a single news site achieved an accuracy of 98.1 % on 3,973 news pages from 12 news sites .
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