An Algorithmic Framework for Accurately Extracting Main Content from News Websites
Main Article Content
Abstract
A new precise MCE algorithm for extracting the main content from news websites is presented. The proposed algorithm uses analysis of the Document Object Model (DOM) structure and content density metrics to identify and extract the informational core of a web page. The implemented approach combines three key features: the maximum number of direct child elements containing text, the maximum textual content without child elements containing text, and the closest position to the average node depth. The algorithm demonstrated superior performance compared to existing solutions such as Boilerpipe and Readability, achieving 99.96% precision, 99.69% recall, and 99.80% F1-score on a comprehensive dataset of 500 diverse web pages. Its language-independent design makes the algorithm particularly effective for extracting multilingual content, including languages with complex structures such as Arabic.
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