# Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments

arXiv: https://arxiv.org/abs/2410.03594 · HTML: https://mlsanity.com/paper/2410.03594/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [Exploring the Feasibility of ChatGPT for Event Extraction](https://mlsanity.com/paper/2303.03836/) (2023)
- [Code4Struct: Code Generation for Few-Shot Event Structure Prediction](https://mlsanity.com/paper/2210.12810/) (2022)
- [PHEE: A Dataset for Pharmacovigilance Event Extraction from Text](https://mlsanity.com/paper/2210.12560/) (2022)
- [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023)
- [DICE: Data-Efficient Clinical Event Extraction with Generative Models](https://mlsanity.com/paper/2208.07989/) (2022)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Event Extraction as Question Generation and Answering](https://mlsanity.com/paper/2307.05567/) (2023)
- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [Coupled Confusion Correction: Learning from Crowds with Sparse Annotations](https://mlsanity.com/paper/2312.07331/) (2023)
- [Rumor Detection on Social Media: Datasets, Methods and Opportunities](https://mlsanity.com/paper/1911.07199/) (2019)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024)
- [Characterizing Information Seeking Events in Health-Related Social Discourse](https://mlsanity.com/paper/2308.09156/) (2023)
- [EMONA: Event-level Moral Opinions in News Articles](https://mlsanity.com/paper/2404.01715/) (2024)
- [Scope of Pre-trained Language Models for Detecting Conflicting Health Information](https://mlsanity.com/paper/2209.11102/) (2022)
- [COVID-19 event extraction from Twitter via extractive question answering with continuous prompts](https://mlsanity.com/paper/2303.10659/) (2023)
- [A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product](https://mlsanity.com/paper/2410.01633/) (2024)

## Built on by (most cited first, 9 of 9)

- [CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation](https://mlsanity.com/paper/2505.20779/) (2025)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction](https://mlsanity.com/paper/2502.16838/) (2025)
- [Can Large Language Models be Effective Online Opinion Miners?](https://mlsanity.com/paper/2505.15695/) (2025)
- [Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators](https://mlsanity.com/paper/2503.06778/) (2025)
- [SciEvent: Benchmarking Multi-domain Scientific Event Extraction](https://mlsanity.com/paper/2509.15620/) (2025)
- [Improving Information Extraction with Learned Queries](https://mlsanity.com/paper/2608.31058/) (2026)
- [Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text](https://mlsanity.com/paper/2606.30914/) (2026)
- [ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives](https://mlsanity.com/paper/2608.06495/) (2026)

## Abstract (arXiv)

Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit -- i.e. assumed to be contiguous spans of text in a document. In this study, we revisit this definition of Event Extraction (EE) by introducing two key argument types that cannot be modeled by existing EE frameworks. First, implicit arguments are event arguments which are not explicitly mentioned in the text, but can be inferred through context. Second, scattered arguments are event arguments that are composed of information scattered throughout the text. These two argument types are crucial to elicit the full breadth of information required for proper event modeling. To support the extraction of explicit, implicit, and scattered arguments, we develop a novel dataset, DiscourseEE, which includes 7,464 argument annotations from online health discourse. Notably, 51.2% of the arguments are implicit, and 17.4% are scattered, making DiscourseEE a unique corpus for complex event extraction. Additionally, we formulate argument extraction as a text generation problem to facilitate the extraction of complex argument types. We provide a comprehensive evaluation of state-of-the-art models and highlight critical open challenges in generative event extraction. Our data and codebase are available at https://omar-sharif03.github.io/DiscourseEE.