Event-Keyed Summarization
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 47 references that are in the map. First posted on arXiv 10 Feb 2024.
Builds on
19 of 47 references.
References · 19
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- 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
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- 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
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- 2020Re-evaluating Evaluation in Text Summarizationcited by 144
- 2017Controllable Abstractive Summarizationcited by 110
7 more
- 2021A Statistical Analysis of Summarization Evaluation Metrics using Resampling Methodscited by 71
- 2020Generating Query Focused Summaries from Query-Free Resourcescited by 27
- 2021LOME: Large Ontology Multilingual Extractioncited by 23
- 2022Iterative Document-level Information Extraction via Imitation Learningcited by 11
- 2022Automatic Error Analysis for Document-level Information Extractioncited by 10
- 2022POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Eventscited by 5
- 2024MultiMUC: Multilingual Template Filling on MUC-4cited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.