  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.
