# Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm

arXiv: https://arxiv.org/abs/2406.16021 · HTML: https://mlsanity.com/paper/2406.16021/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [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)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)
- [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://mlsanity.com/paper/2104.05022/) (2021)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Entity-centered Cross-document Relation Extraction](https://mlsanity.com/paper/2210.16541/) (2022)
- [Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference](https://mlsanity.com/paper/2110.05362/) (2021)
- [Giveme5W1H: A Universal System for Extracting Main Events from News Articles](https://mlsanity.com/paper/1909.02766/) (2019)
- [Multi-hop Evidence Retrieval for Cross-document Relation Extraction](https://mlsanity.com/paper/2212.10786/) (2022)
- [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://mlsanity.com/paper/2210.12654/) (2022)
- [RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction](https://mlsanity.com/paper/2206.03377/) (2022)
- [Graph Refinement for Coreference Resolution](https://mlsanity.com/paper/2203.16574/) (2022)
- [Continual Event Extraction with Semantic Confusion Rectification](https://mlsanity.com/paper/2310.15470/) (2023)
- [Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization](https://mlsanity.com/paper/2305.18926/) (2023)
- [Improve Event Extraction via Self-Training with Gradient Guidance](https://mlsanity.com/paper/2205.12490/) (2022)

## Built on by (most cited first, 2 of 2)

- [Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators](https://mlsanity.com/paper/2503.06778/) (2025)
- [Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize?](https://mlsanity.com/paper/2609.38799/) (2026)

## Abstract (arXiv)

Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of the information source. This paper addresses the limitations of traditional document-level event extraction by proposing the task of cross-document event extraction (CDEE) to integrate event information from multiple documents and provide a comprehensive perspective on events. We construct a novel cross-document event extraction dataset, namely CLES, which contains 20,059 documents and 37,688 mention-level events, where over 70% of them are cross-document. To build a benchmark, we propose a CDEE pipeline that includes 5 steps, namely event extraction, coreference resolution, entity normalization, role normalization and entity-role resolution. Our CDEE pipeline achieves about 72% F1 in end-to-end cross-document event extraction, suggesting the challenge of this task. Our work builds a new line of information extraction research and will attract new research attention.