Content-intensive websites, e.g., of blogs or news, present pages that contain Web articles automatically generated by content management systems. Identification and extraction of their main content is critical in many applications, such as indexing or classification. We present a novel unsupervised approach for the extraction of Web articles from dynamically-generated Web pages. Our system, called FOREST, combines structural and information-based features to target the main content generated by a Web source, and published in associated Web pages. We extensively evaluate FOREST with respect to various baselines and datasets, and report improved results over state-of-the art techniques in content extraction.