WebSRC: A Dataset for Web-Based Structural Reading Comprehension
Xingyu Chen, Zihan Zhao, Lu Chen, Jiabao Ji, Danyang Zhang, Ao Luo, Yuxuan Xiong, Kai Yu · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 Web-SRC, 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 1 .