Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers

arXiv:2403.01061v1 Announce Type: new
Abstract: We evaluate recent Large language Models (LLMs) on the challenging task of summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled timelines. Importantly, we work directly with authors to ensure that the stories have not been shared online (and therefore are unseen by the models), and to obtain informed evaluations of summary quality using judgments from the authors themselves. Through quantitative and qualitative analysis grounded in narrative theory, we compare GPT-4, …

abstract arxiv authors cs.cl language language models large language large language models llms reading stories story summarization summarizing type work writers