Boilerplate Detection via Semantic Classification of TextBlocks

Hao Zhang; Jie Wang

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arXiv · 2203.04467

Boilerplate Detection via Semantic Classification of TextBlocks

Hao Zhang·Jie Wang

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Abstract

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.

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Hao Zhang, Jie Wang. 2022-03-09. Boilerplate Detection via Semantic Classification of TextBlocks. https://arxiv.org/abs/2203.04467

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