Learning Web Content Extraction with DOM Features

Content extraction is the process that aims to separate the main content of web pages from the bulk of template and decorative components. We present a method of doing this which achieves competitive performance on the Cleaneval dataset and sets a new state-of-the-art with an F1 score of 0.96 on the Dragnet dataset. We accomplish this by modeling the task as a classification problem over HTML tags using features based on information from the DOM tree. Not only do we obtain a performance increase over current methods, but we do so with minimal feature engineering and without the extensive preprocessing steps of other methods.