When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.

翻译：阅读文学作品时,读者常常对各种角色的作用、个性、关系、意图、行动等作出推断。虽然人类可以随时利用过去的经验来建立这种以性为中心的叙事观点,但理解叙事中的字符对于机器来说是一项艰巨的任务。为了鼓励对以性为中心的叙事理解领域的研究,我们介绍了LISCU -- -- 一套新的文学作品数据集及其摘要,配有其中出现的人物的描述。我们还介绍了LISCU的两项新任务:特征识别和特征描述生成。我们用为这些任务调整的若干经过预先训练的语言模型进行的实验表明,需要有更好的叙事理解模式。