# Simplified DOM Trees for Transferable Attribute Extraction from the Web

arXiv: https://arxiv.org/abs/2101.02415 · HTML: https://mlsanity.com/paper/2101.02415/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation](https://mlsanity.com/paper/1901.08907/) (2019)
- [TENER: Adapting Transformer Encoder for Named Entity Recognition](https://mlsanity.com/paper/1911.04474/) (2019)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification](https://mlsanity.com/paper/1909.03084/) (2019)
- [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)
- [Fonduer: Knowledge Base Construction from Richly Formatted Data](https://mlsanity.com/paper/1703.05028/) (2017)
- [CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web](https://mlsanity.com/paper/1804.04635/) (2018)

## Built on by (most cited first, 16 of 16)

- [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://mlsanity.com/paper/2306.16527/) (2023)
- [WebLINX: Real-World Website Navigation with Multi-Turn Dialogue](https://mlsanity.com/paper/2402.05930/) (2024)
- [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://mlsanity.com/paper/2110.08518/) (2021)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [DOM-LM: Learning Generalizable Representations for HTML Documents](https://mlsanity.com/paper/2201.10608/) (2022)
- [AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation](https://mlsanity.com/paper/2404.12753/) (2024)
- [SEED: Domain-Specific Data Curation With Large Language Models](https://mlsanity.com/paper/2310.00749/) (2023)
- [Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM](https://mlsanity.com/paper/2511.23119/) (2025)
- [CoVA: Context-aware Visual Attention for Webpage Information Extraction](https://mlsanity.com/paper/2110.12320/) (2021)
- [Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding](https://mlsanity.com/paper/2402.18262/) (2024)
- [PLAtE: A Large-scale Dataset for List Page Web Extraction](https://mlsanity.com/paper/2205.12386/) (2022)
- [The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models](https://mlsanity.com/paper/2111.02168/) (2021)
- [XPath Agent: An Efficient XPath Programming Agent Based on LLM for Web Crawler](https://mlsanity.com/paper/2502.15688/) (2024)
- [Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path](https://mlsanity.com/paper/2305.13805/) (2023)
- [AutoFAIR : Automatic Data FAIRification via Machine Reading](https://mlsanity.com/paper/2408.04673/) (2024)
- [Cross-Domain Web Information Extraction at Pinterest](https://mlsanity.com/paper/2508.01096/) (2025)

## Abstract (arXiv)

There has been a steady need to precisely extract structured knowledge from the web (i.e. HTML documents). Given a web page, extracting a structured object along with various attributes of interest (e.g. price, publisher, author, and genre for a book) can facilitate a variety of downstream applications such as large-scale knowledge base construction, e-commerce product search, and personalized recommendation. Considering each web page is rendered from an HTML DOM tree, existing approaches formulate the problem as a DOM tree node tagging task. However, they either rely on computationally expensive visual feature engineering or are incapable of modeling the relationship among the tree nodes. In this paper, we propose a novel transferable method, Simplified DOM Trees for Attribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving useful context for each node by leveraging the tree structure. We study two challenging experimental settings: (i) intra-vertical few-shot extraction, and (ii) cross-vertical fewshot extraction with out-of-domain knowledge, to evaluate our approach. Extensive experiments on the SWDE public dataset show that SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1 score. We also find that utilizing knowledge from a different vertical (cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a further 1.37%.