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Web2Text: Deep Structured Boilerplate Removal
[article]
2018
arXiv
pre-print
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
arXiv:1801.02607v3
fatcat:wha5oi5hubcurnpddtggzegziy