  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
https://x-lance.github.io/WebSRC/.
