  Web search is an essential way for human 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 web-based structural reading
comprehension. Given a web page and a question about it, the task is to find an
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 0.44M question-answer pairs, which are collected from 6.5K
web pages with corresponding HTML source code, screenshots, and metadata. 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 strong 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 task are publicly available at
https://speechlab-sjtu.github.io/WebSRC/.
