WIERT: Web Information Extraction via Render Tree


  • Zimeng Li Beihang University
  • Bo Shao Microsoft STCA
  • Linjun Shou Microsoft STCA
  • Ming Gong Microsoft STCA
  • Gen Li Microsoft STCA
  • Daxin Jiang Microsoft STCA




SNLP: Information Extraction, KRR: Knowledge Engineering, SNLP: Applications, SNLP: Language Models, SNLP: Question Answering, SNLP: Speech and Multimodality, SNLP: Text Mining


Web information extraction (WIE) is a fundamental problem in web document understanding, with a significant impact on various applications. Visual information plays a crucial role in WIE tasks as the nodes containing relevant information are often visually distinct, such as being in a larger font size or having a brighter color, from the other nodes. However, rendering visual information of a web page can be computationally expensive. Previous works have mainly focused on the Document Object Model (DOM) tree, which lacks visual information. To efficiently exploit visual information, we propose leveraging the render tree, which combines the DOM tree and Cascading Style Sheets Object Model (CSSOM) tree, and contains not only content and layout information but also rich visual information at a little additional acquisition cost compared to the DOM tree. In this paper, we present WIERT, a method that effectively utilizes the render tree of a web page based on a pretrained language model. We evaluate WIERT on the Klarna product page dataset, a manually labeled dataset of renderable e-commerce web pages, demonstrating its effectiveness and robustness.




How to Cite

Li, Z., Shao, B., Shou, L., Gong, M., Li, G., & Jiang, D. (2023). WIERT: Web Information Extraction via Render Tree. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 13166-13173. https://doi.org/10.1609/aaai.v37i11.26546



AAAI Technical Track on Speech & Natural Language Processing