WebFormer：用于结构信息抽取的网页Transformer
WebFormer: The Web-page Transformer for Structure Information Extraction
摘要：
结构信息抽取是指从网页中抽取结构化文本字段的任务，例如从购物页面中抽取产品报价，包括产品标题、描述、品牌和价格。它是文档理解和Web搜索领域的一个重要研究课题。最近的自然语言模型与序列建模已经证明了国家的最先进的性能在Web信息抽取。然而，有效地序列化令牌从非结构化的网页是具有挑战性的，在实践中，由于各种各样的网页布局模式。有限的工作集中在为提取文本字段而对Web布局建模上。在本文中，我们介绍了WebFormer，一个网页Transformer模型的结构信息抽取从Web文档。首先，我们为HTML中的每个DOM节点设计HTML标记，通过图形注意力嵌入其相邻标记的表示。其次，我们在HTML标记和文本标记之间构建了丰富的注意力模式，从而利用Web布局进行有效的注意力权重计算。我们进行了广泛的实验SWDE和常见的爬行基准。实验结果表明，所提出的方法优于几个国家的最先进的方法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
