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We propose a new task for assessing machines' skills of understandingfictional characters in narrative stories. The task, TVShowGuess, builds on thescripts of TV series and takes the form of guessing the anonymous maincharacters based on the backgrounds of the scenes and the dialogues. Our humanstudy supports that this form of task covers comprehension of multiple types ofcharacter persona, including understanding characters' personalities, facts andmemories of personal experience, which are well aligned with the psychologicaland literary theories about the theory of mind (ToM) of human beings onunderstanding fictional characters during reading. We further propose new modelarchitectures to support the contextualized encoding of long scene texts.Experiments show that our proposed approaches significantly outperformbaselines, yet still largely lag behind the (nearly perfect) human performance.Our work serves as a first step toward the goal of narrative charactercomprehension.

TVShowGuess: Character Comprehension in Stories as Speaker Guessing