Structure information extraction refers to the task of extracting structured
text fields from web pages, such as extracting a product offer from a shopping
page including product title, description, brand and price. It is an important
research topic which has been widely studied in document understanding and web
search. Recent natural language models with sequence modeling have demonstrated
state-of-the-art performance on web information extraction. However,
effectively serializing tokens from unstructured web pages is challenging in
practice due to a variety of web layout patterns. Limited work has focused on
modeling the web layout for extracting the text fields. In this paper, we
introduce WebFormer, a Web-page transFormer model for structure information
extraction from web documents. First, we design HTML tokens for each DOM node
in the HTML by embedding representations from their neighboring tokens through
graph attention. Second, we construct rich attention patterns between HTML
tokens and text tokens, which leverages the web layout for effective attention
weight computation. We conduct an extensive set of experiments on SWDE and
Common Crawl benchmarks. Experimental results demonstrate the superior
performance of the proposed approach over several state-of-the-art methods.

本文介绍了 WebFormer，一种从 Web 文档中提取结构化信息的 Web 页面转换模型，借助自然语言模型和图注意力等技术，实现了序列化难以提取出的 Web 页面标记数据的结构化信息提取，通过 SWDE 和 Common Crawl 测试基准得出了比其他先进方法更优越的实验结果。