  We present a hierarchical neural network model called SemText to detect HTML
boilerplate based on a novel semantic representation of HTML tags, class names,
and text blocks. We train SemText on three published datasets of news webpages
and fine-tune it using a small number of development data in CleanEval and
GoogleTrends-2017. We show that SemText achieves the state-of-the-art accuracy
on these datasets. We then demonstrate the robustness of SemText by showing
that it also detects boilerplate effectively on out-of-domain community-based
question-answer webpages.
