# A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents

arXiv: https://arxiv.org/abs/2604.21885 · HTML: https://mlsanity.com/paper/2604.21885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [CLIP-Event: Connecting Text and Images with Event Structures](https://mlsanity.com/paper/2201.05078/) (2022)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [GRAG: Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2405.16506/) (2024)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding](https://mlsanity.com/paper/2005.06579/) (2020)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies](https://mlsanity.com/paper/2010.12787/) (2020)
- [UMIE: Unified Multimodal Information Extraction with Instruction Tuning](https://mlsanity.com/paper/2401.03082/) (2024)
- [Giveme5W1H: A Universal System for Extracting Main Events from News Articles](https://mlsanity.com/paper/1909.02766/) (2019)
- [Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation](https://mlsanity.com/paper/2005.11355/) (2020)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)

## Abstract (arXiv)

Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and thus rarely generalize to unseen types and (2) open-domain event extraction algorithms, capable of handling unconstrained event types, have largely overlooked the potential of large language models (LLMs) despite their advanced abilities. Additionally, they do not explicitly model document-level contextual, structural, and semantic reasoning, which are crucial for effective event extraction but remain challenging for LLMs due to lost-in-the-middle phenomenon and attention dilution. To address these limitations, we propose multimodal open-domain event extraction, MODEE , a novel approach for open-domain event extraction that combines graph-based learning with text-based representation from LLMs to model document-level reasoning. Empirical evaluations on large datasets demonstrate that MODEE outperforms state-of-the-art open-domain event extraction approaches and can be generalized to closed-domain event extraction, where it outperforms existing algorithms.