# Hypertext Entity Extraction in Webpage

arXiv: https://arxiv.org/abs/2403.01698 · HTML: https://mlsanity.com/paper/2403.01698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Orthogonal Projection Loss](https://mlsanity.com/paper/2103.14021/) (2021)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages](https://mlsanity.com/paper/2005.07105/) (2020)
- [FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents](https://mlsanity.com/paper/2010.10755/) (2020)

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

- [LiveWeb-IE: A Benchmark For Online Web Information Extraction](https://mlsanity.com/paper/2603.13773/) (2026)

## Abstract (arXiv)

Webpage entity extraction is a fundamental natural language processing task in both research and applications. Nowadays, the majority of webpage entity extraction models are trained on structured datasets which strive to retain textual content and its structure information. However, existing datasets all overlook the rich hypertext features (e.g., font color, font size) which show their effectiveness in previous works. To this end, we first collect a Hypertext Entity Extraction Dataset (textitHEED) from the e-commerce domains, scraping both the text and the corresponding explicit hypertext features with high-quality manual entity annotations. Furthermore, we present the MoE-based Entity Extraction Framework (textitMoEEF), which efficiently integrates multiple features to enhance model performance by Mixture of Experts and outperforms strong baselines, including the state-of-the-art small-scale models and GPT-3.5-turbo. Moreover, the effectiveness of hypertext features in textitHEED and several model components in textitMoEEF are analyzed.