Akankshya Mishra
2024
STORYSUMM: Evaluating Faithfulness in Story Summarization
Melanie Subbiah
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Faisal Ladhak
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Akankshya Mishra
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Griffin Thomas Adams
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Lydia Chilton
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Kathleen McKeown
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
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.
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