# Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction

arXiv: https://arxiv.org/abs/2405.01884 · HTML: https://mlsanity.com/paper/2405.01884/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction](https://mlsanity.com/paper/2202.12109/) (2022)
- [AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model](https://mlsanity.com/paper/2305.16734/) (2023)
- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction](https://mlsanity.com/paper/2205.14847/) (2022)
- [Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?](https://mlsanity.com/paper/2306.00502/) (2023)
- [Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models](https://mlsanity.com/paper/2311.07314/) (2023)
- [An AMR-based Link Prediction Approach for Document-level Event Argument Extraction](https://mlsanity.com/paper/2305.19162/) (2023)
- [LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction](https://mlsanity.com/paper/2311.06555/) (2023)
- [From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction](https://mlsanity.com/paper/2310.16358/) (2023)

## Built on by (most cited first, 12 of 12)

- [SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers](https://mlsanity.com/paper/2504.00255/) (2025)
- [Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies](https://mlsanity.com/paper/2503.00724/) (2025)
- [XMeCap: Meme Caption Generation with Sub-Image Adaptability](https://mlsanity.com/paper/2407.17152/) (2024)
- [BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records](https://mlsanity.com/paper/2407.05213/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators](https://mlsanity.com/paper/2503.06778/) (2025)
- [Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction](https://mlsanity.com/paper/2603.02909/) (2026)
- [EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain](https://mlsanity.com/paper/2406.14075/) (2024)
- [Event Argument Extraction with Enriched Prompts](https://mlsanity.com/paper/2501.06825/) (2025)
- [MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG](https://mlsanity.com/paper/2503.13563/) (2025)
- [DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation](https://mlsanity.com/paper/2507.13957/) (2025)
- [Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction](https://mlsanity.com/paper/2508.19359/) (2025)

## Abstract (arXiv)

Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneouslyThe proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.