@inproceedings{gantt-etal-2024-event,
title = "Event-Keyed Summarization",
author = "Gantt, William and
Martin, Alexander and
Kuchmiichuk, Pavlo and
White, Aaron",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.431",
pages = "7333--7345",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Event-Keyed Summarization
%A Gantt, William
%A Martin, Alexander
%A Kuchmiichuk, Pavlo
%A White, Aaron
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F gantt-etal-2024-event
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.431
%P 7333-7345
Markdown (Informal)
[Event-Keyed Summarization](https://aclanthology.org/2024.findings-emnlp.431) (Gantt et al., Findings 2024)
ACL
- William Gantt, Alexander Martin, Pavlo Kuchmiichuk, and Aaron White. 2024. Event-Keyed Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7333–7345, Miami, Florida, USA. Association for Computational Linguistics.