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

• URL: http://arxiv.org/abs/2403.01061v3
• Date: Thu, 11 Jul 2024 18:46:52 GMT
• Title: Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers
• Authors: Melanie Subbiah, Sean Zhang, Lydia B. Chilton, Kathleen McKeown,
• Abstract summary: We evaluate recent Large Language Models (LLMs) on the challenging task of summarizing short stories. We work directly with authors to ensure that the stories have not been shared online (and therefore are unseen by the models) We compare GPT-4, Claude-2.1, and LLama-2-70B and find that all three models make faithfulness mistakes in over 50% of summaries.
• Score: 25.268709339109893
• License: http://creativecommons.org/licenses/by/4.0/
• 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, Claude-2.1, and LLama-2-70B. We find that all three models make faithfulness mistakes in over 50% of summaries and struggle with specificity and interpretation of difficult subtext. We additionally demonstrate that LLM ratings and other automatic metrics for summary quality do not correlate well with the quality ratings from the writers.

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