%0 Conference Paper
%A Wang, Junfeng
%A He, Xiaofei
%A Wang, Can
%A Pei, Jian
%A Bu, Jiajun
%A Chen, Chun
%A Guan, Ziyu
%A Gang, Lu
%B 18th International World Wide Web Conference
%C Madrid, Spain
%D 2009
%F www2009:123
%P 1085-1085
%T News Article Extraction with Template-Independent Wrapper
%U http://www2009.eprints.org/123/
%X 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 cannot 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.