# Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies

arXiv: https://arxiv.org/abs/2010.12787 · HTML: https://mlsanity.com/paper/2010.12787/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction](https://mlsanity.com/paper/2202.12109/) (2022)
- [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021)
- [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023)
- [TAGPRIME: A Unified Framework for Relational Structure Extraction](https://mlsanity.com/paper/2205.12585/) (2022)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Improving Event Definition Following For Zero-Shot Event Detection](https://mlsanity.com/paper/2403.02586/) (2024)
- [DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction](https://mlsanity.com/paper/2310.10481/) (2023)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [Towards Event Extraction from Speech with Contextual Clues](https://mlsanity.com/paper/2401.15385/) (2024)
- [CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities](https://mlsanity.com/paper/2310.14512/) (2023)
- [LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence](https://mlsanity.com/paper/2603.01651/) (2026)
- [Causal Intervention-based Prompt Debiasing for Event Argument Extraction](https://mlsanity.com/paper/2210.01561/) (2022)
- [Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge](https://mlsanity.com/paper/2303.16751/) (2023)
- [A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21885/) (2026)
- [NEST: Narrative Event Structures in Time for Long Video Understanding](https://mlsanity.com/paper/2606.19706/) (2026)

## Abstract (arXiv)

Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.