Story Summarization

Story summarization research focuses on automatically generating concise and faithful summaries of narratives, tackling challenges like handling nuanced subtext, complex timelines, and maintaining narrative coherence. Current efforts involve developing and evaluating large language models (LLMs) and hierarchical models, often using newly created datasets with fine-grained annotations for faithfulness and coherence assessment. This work is crucial for improving natural language understanding and generation, with applications ranging from efficient information retrieval to enhanced accessibility of long-form content.