Human evaluation has been the gold standard for checking faithfulness in abstractive summarization. However, with a challenging source domain like narrative, multiple annotators can agree a summary is faithful, while missing details that are obvious errors only once pointed out. We therefore introduce a new dataset, STORYSUMM, comprising LLM summaries of short stories with localized faithfulness labels and error explanations. This benchmark is for evaluation methods, testing whether a given method can detect challenging inconsistencies. Using this dataset, we first show that any one human annotation protocol is likely to miss inconsistencies, and we advocate for pursuing a range of methods when establishing ground truth for a summarization dataset. We finally test recent automatic metrics and find that none of them achieve more than 70% balanced accuracy on this task, demonstrating that it is a challenging benchmark for future work in faithfulness evaluation.
View on arXiv@article{subbiah2025_2407.06501,
title={ STORYSUMM: Evaluating Faithfulness in Story Summarization },
author={ Melanie Subbiah and Faisal Ladhak and Akankshya Mishra and Griffin Adams and Lydia B. Chilton and Kathleen McKeown },
journal={arXiv preprint arXiv:2407.06501},
year={ 2025 }
}