"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding

TL;DR

This paper introduces LiSCU, a new dataset for character-centric narrative understanding, along with two tasks, highlighting the challenges in machine comprehension of literary characters.

Contribution

The paper presents LiSCU, a novel dataset and two tasks for character-focused narrative understanding, fostering research in this underexplored area.

Findings

Pre-trained models struggle with character identification.

Models need improvement for character description generation.

LiSCU enables benchmarking of narrative comprehension models.

Abstract

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.