Identifying Content Blocks from Web Documents

Intelligent information processing systems, such as digital libraries or search engines index web-pages according to their informative content. However, web-pages contain several non-informative contents, e.g., navigation sidebars, advertisements, copyright notices, etc. It is very important to separate the informative “primary content blocks” from these non-informative blocks. In this paper, two algorithms, FeatureExtractor and K-FeatureExtractor are proposed to identify the “primary content blocks” based on their features. None of these algorithms require any supervised learning, but still can identify the “primary content blocks” with high precision and recall. While operating on several thousand web-pages obtained from 15 different websites, our algorithms significantly outperform the Entropy-based algorithm proposed by Lin and Ho [14] in both precision and run-time.