EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain (2026.acl-long)
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| Challenge: | Extensive event extraction research has been conducted in many domains, including news, finance, and biology. |
| Approach: | They propose an end-to-end scientific event extraction framework for encoding nuggets into a grid matrix and simplifying complex event extraction as a nuggot-based grid modeling task. |
| Outcome: | The proposed framework performs well in scientific domain, demonstrating state-of-the-art performance. |
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| Challenge: | Existing work on scientific information extraction relies on entity-relation extraction in narrow domains . current models struggle in domains such as sociology and humanities . |
| Approach: | They propose a multi-domain benchmark for scientific abstract annotations using a unified event extraction schema. |
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Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
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DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)
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| Challenge: | Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model . |
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OpenForecast: A Large-Scale Open-Ended Event Forecasting Dataset (2025.coling-main)
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| Challenge: | Existing closed-ended event forecasting methods are constrained by a limited answer space. |
| Approach: | They introduce OpenForecast, a large-scale open-ended dataset with three open-ending event forecasting tasks and an automatic LLM-based method for complex events. |
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Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
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DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)
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| Challenge: | Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets. |
| Approach: | They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments. |
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Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)
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| Challenge: | Document-level event extraction aims to extract structured information from unstructured text. |
| Approach: | They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events. |
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Event-Keyed Summarization (2024.findings-emnlp)
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| Challenge: | a novel task combines document-level event extraction with event-keyed summarization . a recent study has shown that traditional summarizing produces inferior summaries of target events . |
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Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
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Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |