Learning to Extract Content from News Webpages

We consider the problem of content extraction from online news webpages. To explore to what extent the syntactic markup and the visual structure of a webpage facilitate the extraction of its content, we compare two state-of-theart classifiers as first instantiations of a general framework that allows for proper model comparison. To this end, we introduce the publicly available NEWS600 corpus, a set of 604 real world news webpages which have been annotated with 30 semantic labels. An empirical analysis of the two models on this dataset shows that the inclusion of structural information is indeed advantageous.

• Type: Conference
• Year: 2009
• Where: AINA
• Authors: Alex Spengler, Patrick Gallinari
• Keywords: AINA 2009, Computer Networks, Content Extraction, Proper Model Comparison, first Instantiations