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Recently, the structural reading comprehension (SRC) task on web pages hasattracted increasing research interests. Although previous SRC work hasleveraged extra information such as HTML tags or XPaths, the informativetopology of web pages is not effectively exploited. In this work, we propose aTopological Information Enhanced model (TIE), which transforms the token-leveltask into a tag-level task by introducing a two-stage process (i.e. nodelocating and answer refining). Based on that, TIE integrates Graph AttentionNetwork (GAT) and Pre-trained Language Model (PLM) to leverage the topologicalinformation of both logical structures and spatial structures. Experimentalresults demonstrate that our model outperforms strong baselines and achievesstate-of-the-art performances on the web-based SRC benchmark WebSRC at the timeof writing. The code of TIE will be publicly available athttps://github.com/X-LANCE/TIE.

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages