跨文档事件摘要新任务，基于多源信息生成事件总结。
Cross-Document Event-Keyed Summarization
- 从多篇文档中融合同一事件的信息生成摘要。
- 构建高质量数据集SEAMUS，支持跨文档事件摘要评估。
- 适合作为大模型在多源信息整合任务的基准测试
事件键摘要（EKS）要求根据文档文本和从中提取的事件表示，对特定事件进行摘要。本文将EKS扩展至跨文档设置（CDEKS），即摘要需综合多个来源对同一事件的描述。我们引入SEAMUS（多源事件摘要）数据集，该数据集基于对FAMUS数据集的专家重新标注，用于跨文档论点抽取任务。我们在SEAMUS上提出一系列基线模型，涵盖小型微调模型及零样本、少样本提示的大语言模型，并进行详细消融分析与人工评估，证明SEAMUS是该新兴任务的可靠基准。
原文摘要 · Abstract (English)
Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce SEAMUS (Summaries of Events Across Multiple Sources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMUS dataset for cross-document argument extraction. We present a suite of baselines on SEAMUS -- covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs -- along with detailed ablations and a human evaluation study, showing SEAMUS to be a valuable benchmark for this new task.
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