Recently, the structural reading comprehension (SRC) task on web pages has
attracted increasing research interests. Although previous SRC work has
leveraged extra information such as HTML tags or XPaths, the informative
topology of web pages is not effectively exploited. In this work, we propose a
Topological Information Enhanced model (TIE), which transforms the token-level
task into a tag-level task by introducing a two-stage process (i.e. node
locating and answer refining). Based on that, TIE integrates Graph Attention
Network (GAT) and Pre-trained Language Model (PLM) to leverage the topological
information of both logical structures and spatial structures. Experimental
results demonstrate that our model outperforms strong baselines and achieves
state-of-the-art performances on the web-based SRC benchmark WebSRC at the time
of writing. The code of TIE will be publicly available at
this https URL.

本文提出了一种基于网络页面拓扑信息的阅读理解模型 TIE，通过将令牌级任务转化为标签级任务，引入了两个步骤（即节点定位和答案细化），并整合了图形注意力网络和预训练语言模型，实验结果表明其性能优于强基线并达到了最新水平。

TIE: 基于拓扑信息增强的网络页面结构阅读理解

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages

Web search is an essential way for humans to obtain information, but it's
still a great challenge for machines to understand the contents of web pages.
In this paper, we introduce the task of structural reading comprehension (SRC)
on web. Given a web page and a question about it, the task is to find the
answer from the web page. This task requires a system not only to understand
the semantics of texts but also the structure of the web page. Moreover, we
proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset.
WebSRC consists of 400K question-answer pairs, which are collected from 6.4K
web pages. Along with the QA pairs, corresponding HTML source code,
screenshots, and metadata are also provided in our dataset. Each question in
WebSRC requires a certain structural understanding of a web page to answer, and
the answer is either a text span on the web page or yes/no. We evaluate various
baselines on our dataset to show the difficulty of our task. We also
investigate the usefulness of structural information and visual features. Our
dataset and baselines have been publicly available at
this https URL

本文提出了结构化阅读理解（SRC）的概念和挑战，介绍了基于 Web 的 SRC 任务和其难点，以及开发 WebSRC 数据集的方法和性质。WebSRC 包括 400K 个问题 - 答案对，涵盖了 6.4K 个 Web 页面，并公开了相应的 HTML 源代码、屏幕截图和元数据。该数据集对于文章的理解不仅需要理解文本语义，还需理解文章的结构。该文还进行了多种基础模型的评估，并探讨了结构信息和视觉特征的有用性。