Boilerplate Removal using a Neural Sequence Labeling Model
This addresses the need for generalizable content extraction in applications like browser reader views and information retrieval, though it is incremental as it builds on existing neural approaches.
The paper tackles the problem of extracting main content from web pages by proposing a neural sequence labeling model that uses only HTML tags and words as input, eliminating the need for hand-crafted features. It demonstrates this model outperforms state-of-the-art methods on a new dataset and adapts to changes in web page structure.
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.