Article clipper: a system for web article extraction.

Many people use the Web as the main source of information in their daily lives. However, most web pages contain non-informative components such as side bars, footers, headers, and advertisements, which are undesirable for certain applications like printing. We demonstrate a system that automatically extracts the informative contents from news- and blog-like web pages. In contrast to many existing methods that are limited to identifying only the text or the bounding rectangular region, our system not only identifies the content but also the structural roles of various content components such as title, paragraphs, images and captions. The structural information enables re-layout of the content in a pleasing way. Besides the article text extraction, our system includes the following components: 1) print-link detection to identify the URL link for printing, and to use it for more reliable analysis and recognition; 2) title detection incorporating both visual cues and HTML tags; 3) image and caption detection utilizing extensive visual cues; 4) multiple-page and next page URL detection. The performance of our system has been thoroughly evaluated using a human labeled ground truth dataset consisting of 2000 web pages from 100 major web sites. We show accurate results using such a dataset.