Existing factual consistency evaluation approaches for text summarization
provide binary predictions and limited insights into the weakness of
summarization systems. Therefore, we propose the task of fine-grained
inconsistency detection, the goal of which is to predict the fine-grained types
of factual errors in a summary. Motivated by how humans inspect factual
inconsistency in summaries, we propose an interpretable fine-grained
inconsistency detection model, FineGrainFact, which explicitly represents the
facts in the documents and summaries with semantic frames extracted by semantic
role labeling, and highlights the related semantic frames to predict
inconsistency. The highlighted semantic frames help verify predicted error
types and correct inconsistent summaries. Experiment results demonstrate that
our model outperforms strong baselines and provides evidence to support or
refute the summary.

研究提出了 fine-grained inconsistency detection 任务，用于检测文本摘要中的细粒度事实错误类型。通过提出的 FineGrainFact 方法，结合语义帧和语义角色标记来实现明确的事实表示、错误预测和总结改正。结果表明，该模型表现优于强基线模型，为摘要提供了支持或反驳的证据。