  Large language models (LLMs) have demonstrated impressive performance and
spurred numerous AI applications, in which role-playing agents (RPAs) are
particularly popular, especially for fictional characters. The prerequisite for
these RPAs lies in the capability of LLMs to understand characters from
fictional works. Previous efforts have evaluated this capability via basic
classification tasks or characteristic imitation, failing to capture the
nuanced character understanding with LLMs. In this paper, we propose evaluating
LLMs' character understanding capability via the character profiling task,
i.e., summarizing character profiles from corresponding materials, a widely
adopted yet understudied practice for RPA development. Specifically, we
construct the CroSS dataset from literature experts and assess the generated
profiles by comparing ground truth references and their applicability in
downstream tasks. Our experiments, which cover various summarization methods
and LLMs, have yielded promising results. These results strongly validate the
character understanding capability of LLMs. Resources are available at
https://github.com/Joanna0123/character_profiling.
