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.
@article{arxiv.2410.14795,
title = {Cross-Document Event-Keyed Summarization},
author = {William Walden and Pavlo Kuchmiichuk and Alexander Martin and Chihsheng Jin and Angela Cao and Claire Sun and Curisia Allen and Aaron Steven White},
journal= {arXiv preprint arXiv:2410.14795},
year = {2024}
}