We introduce event-keyed summarization (EKS), a novel task that marries
traditional summarization and document-level event extraction, with the goal of
generating a contextualized summary for a specific event, given a document and
an extracted event structure. We introduce a dataset for this task, MUCSUM,
consisting of summaries of all events in the classic MUC-4 dataset, along with
a set of baselines that comprises both pretrained LM standards in the
summarization literature, as well as larger frontier models. We show that
ablations that reduce EKS to traditional summarization or structure-to-text
yield inferior summaries of target events and that MUCSUM is a robust benchmark
for this task. Lastly, we conduct a human evaluation of both reference and
model summaries, and provide some detailed analysis of the results.

介绍了事件关键摘要（EKS），它是传统摘要和文档级事件提取的结合，目标是根据文档和提取的事件结构为特定事件生成一个上下文化的摘要。介绍了这一任务的数据集 MUCSUM，包括经典 MUC-4 数据集中所有事件的摘要，以及预训练语言模型和更大前沿模型组成的基准集。通过减少 EKS 到传统摘要或结构转文本的消融实验，我们证明了目标事件的摘要质量会降低，而 MUCSUM 是这一任务的一个稳健的基准。最后，我们对参考摘要和模型摘要进行了人工评估，并对结果进行了详细分析。