STORYSUMM: Evaluating Faithfulness in Story Summarization

• URL: http://arxiv.org/abs/2407.06501v1
• Date: Tue, 9 Jul 2024 02:06:30 GMT
• Title: STORYSUMM: Evaluating Faithfulness in Story Summarization
• Authors: Melanie Subbiah, Faisal Ladhak, Akankshya Mishra, Griffin Adams, Lydia B. Chilton, Kathleen McKeown,
• Abstract summary: We introduce a new dataset, STORYSUMM, comprising short stories with localized faithfulness labels and error explanations. This benchmark is for evaluation methods, testing whether a given method can detect challenging inconsistencies.
• Score: 31.94902013480574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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