# Boilerplate Detection via Semantic Classification of TextBlocks

arXiv: https://arxiv.org/abs/2203.04467 · HTML: https://mlsanity.com/paper/2203.04467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Boilerplate Removal using a Neural Sequence Labeling Model](https://mlsanity.com/paper/2004.14294/) (2020)
- [Web2Text: Deep Structured Boilerplate Removal](https://mlsanity.com/paper/1801.02607/) (2018)

## Built on by (most cited first, 1 of 1)

- [An Index-based Approach for Efficient and Effective Web Content Extraction](https://mlsanity.com/paper/2512.06641/) (2025)

## Abstract (arXiv)

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.