An Algorithmic Framework to Extract Web Page Core Information

A new, accurate MCE algorithm for extracting core content from news websites is presented. The proposed algorithm leverages Document Object Model (DOM) structure analysis and content density metrics to identify and extract the core information of a web page. The implemented approach combines three key features: the maximum number of direct text-containing child elements, the maximum text content without text-containing child elements, and the closest location to the average node depth. The algorithm demonstrated better performance compared to existing solutions such as Boilerpipe and Readability, reaching 99.96% precision, 99.69% recall, and 99.80% F1-score on a comprehensive dataset of 500 diverse web pages. The language-independent design makes the algorithm particularly effective for extracting multilingual content, including languages with complex structures, such as Arabic.