Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)
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| Challenge: | Existing event extraction methods are limited to extract event arguments within the sentence scope. |
| Approach: | They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively. |
| Outcome: | The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively. |
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DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)
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| Challenge: | Chinese document-level event extraction is still largely unexplored. |
| Approach: | They propose a Chinese document-level event extraction dataset with over 36,000 events and 210,000 arguments. |
| Outcome: | The proposed dataset includes over 36,000 events and more than 210,000 arguments . it is an extension of the DocEE dataset, utilizing the same event schema and annotated by human experts. |
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)
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| Challenge: | Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system. |
| Approach: | They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news. |
| Outcome: | The proposed system detects event mentions and extracts events from financial news . it can generate large scale labeled data and extract events from entire document . |
Revisiting the Evaluation of End-to-end Event Extraction (2021.findings-acl)
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| Challenge: | Existing EE research uses the role-averaged evaluation metric, but it is misleading to downstream applications. |
| Approach: | They propose two new evaluation metrics that explicitly penalize wrongly identified event arguments. |
| Outcome: | The proposed evaluation metrics improve the initial evaluation by 10% . the proposed training scheme is better than the existing one, the authors show . |
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 . |
| Approach: | They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. |
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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. |
| Approach: | They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types. |
| Outcome: | The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages. |
Document-level Event Extraction via Parallel Prediction Networks (2021.acl-long)
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| Challenge: | Document-level event extraction (DEE) is indispensable when events are described throughout a document. |
| Approach: | They propose a document-level event extraction model that can extract structured events from a text in parallel. |
| Outcome: | The proposed model outperforms current state-of-the-art methods on a document-level event extraction task. |
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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OEE-CFC: A Dataset for Open Event Extraction from Chinese Financial Commentary (2024.findings-emnlp)
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Qizhi Wan, Changxuan Wan, Rong Hu, Dexi Liu, Xu Wenwu, Kang Xu, Zou Meihua, Liu Tao, Jie Yang, Zhenwei Xiong
| Challenge: | Existing corpora with unconventional entities serving as event arguments lack rich multi-events and shared arguments. |
| Approach: | They develop an open event template that includes 21 event argument roles and an open corpus supporting open event extraction. |
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Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)
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Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen
| Challenge: | mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events. |
| Approach: | They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously. |
| Outcome: | The proposed model performs better on four public datasets while saving time. |
Exploring Sentence Community for Document-Level Event Extraction (2021.findings-emnlp)
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| Challenge: | Existing approaches to document-level event extraction neglect the complex logic structures in long texts. |
| Approach: | They propose a framework that exploits the relationship between sentences to extract multiple events by sentence community detection using graph attention networks. |
| Outcome: | The proposed framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset. |