Cross-Document Event-Keyed Summarization
Abstract: 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** (**S**ummaries of **E**vents **A**cross **Mu**ltiple **S**ources), 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.
Paper Type: Long
Research Area: Summarization
Research Area Keywords: abstractive summarisation, query-focused summarization, multi-document summarization, few-shot summarisation, event extraction, document-level extraction
Contribution Types: NLP engineering experiment, Data resources
Languages Studied: English
Submission Number: 1172
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