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Deep Structured Boilerplate Removal

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Vogels T., Ganea O. E., Eickhoff C,
Project Conversational Agent forInteractive Access to Information
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Proceedings (peer-reviewed)

Title of proceedings Advances in Information Retrieval 40th European Conference on IR Research ECIR 2018
Place Grenoble, France

Abstract

Web pages are a valuable source of information for many natural language processing and information retrieval tasks. Extracting the main content from those documents is essential for the performance of derived applications. To address this issue, we introduce a novel model that performs sequence labeling to collectively classify all text blocks in an HTML page as either boilerplate or main content. Our method uses a hidden Markov model on top of potentials derived from DOM tree features using convolutional neural networks. The proposed method sets a new state-of-the-art performance for boilerplate removal on the CleanEval benchmark. As a component of information retrieval pipelines, it improves retrieval performance on the ClueWeb12 collection.
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