# Giveme5W1H: A Universal System for Extracting Main Events from News Articles

arXiv: https://arxiv.org/abs/1909.02766 · HTML: https://mlsanity.com/paper/1909.02766/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://mlsanity.com/paper/2311.09105/) (2023)
- [5W1H Extraction With Large Language Models](https://mlsanity.com/paper/2405.16150/) (2024)
- [ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation](https://mlsanity.com/paper/2106.10862/) (2021)
- [American Hate Crime Trends Prediction with Event Extraction](https://mlsanity.com/paper/2111.04951/) (2021)
- [Neural News Recommendation with Event Extraction](https://mlsanity.com/paper/2111.05068/) (2021)
- [Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm](https://mlsanity.com/paper/2406.16021/) (2024)
- [DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory](https://mlsanity.com/paper/2605.15759/) (2026)
- [Semi-Supervised Graph Attention Networks for Event Representation Learning](https://mlsanity.com/paper/2201.00363/) (2022)
- [GlobalNER: Incorporating Non-local Information into Named Entity Recognition](https://mlsanity.com/paper/2303.02915/) (2023)
- [Automated Journalistic Questions: A New Method for Extracting 5W1H in French](https://mlsanity.com/paper/2505.14804/) (2025)
- [A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21885/) (2026)
- [EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21890/) (2026)
- [ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints](https://mlsanity.com/paper/2609.30184/) (2026)
- [TAGGRAPH: Tag-Augmented Graphs for Graph Retrieval of Agent Persistent Histories](https://mlsanity.com/paper/2609.38353/) (2026)

## Abstract (arXiv)

Event extraction from news articles is a commonly required prerequisite for various tasks, such as article summarization, article clustering, and news aggregation. Due to the lack of universally applicable and publicly available methods tailored to news datasets, many researchers redundantly implement event extraction methods for their own projects. The journalistic 5W1H questions are capable of describing the main event of an article, i.e., by answering who did what, when, where, why, and how. We provide an in-depth description of an improved version of Giveme5W1H, a system that uses syntactic and domain-specific rules to automatically extract the relevant phrases from English news articles to provide answers to these 5W1H questions. Given the answers to these questions, the system determines an article's main event. In an expert evaluation with three assessors and 120 articles, we determined an overall precision of p=0.73, and p=0.82 for answering the first four W questions, which alone can sufficiently summarize the main event reported on in a news article. We recently made our system publicly available, and it remains the only universal open-source 5W1H extractor capable of being applied to a wide range of use cases in news analysis.