  Deep text understanding, which requires the connections between a given
document and prior knowledge beyond its text, has been highlighted by many
benchmarks in recent years. However, these benchmarks have encountered two
major limitations. On the one hand, most of them require human annotation of
knowledge, which leads to limited knowledge coverage. On the other hand, they
usually use choices or spans in the texts as the answers, which results in
narrow answer space. To overcome these limitations, we build a new challenging
benchmark named KoRc in this paper. Compared with previous benchmarks, KoRC has
two advantages, i.e., broad knowledge coverage and flexible answer format.
Specifically, we utilize massive knowledge bases to guide annotators or large
language models (LLMs) to construct knowledgable questions. Moreover, we use
labels in knowledge bases rather than spans or choices as the final answers. We
test state-of-the-art models on KoRC and the experimental results show that the
strongest baseline only achieves 68.3% and 30.0% F1 measure in the
in-distribution and out-of-distribution test set, respectively. These results
indicate that deep text understanding is still an unsolved challenge. The
benchmark dataset, leaderboard, and baseline methods are released in
https://github.com/THU-KEG/KoRC.
