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.

Similar Papers

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.
Outcome: The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings.
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.
Outcome: The proposed dataset has over 56,000+ events and 242,000+ arguments.
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.
BKEE: Pioneering Event Extraction in the Vietnamese Language (2024.lrec-main)

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Challenge: Event Extraction (EE) is a fundamental task in information extraction.
Approach: They propose a Vietnamese event extraction dataset that includes 33 different event types and 28 different event argument roles.
Outcome: The proposed dataset provides a labeled dataset for entity mentions, event mentions and event arguments on 1066 documents.
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)

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Challenge: Recent studies suggest that event extraction evaluations may not accurately reflect the true performance.
Approach: They propose a standardized, fair, and reproducible benchmark for event extraction . they use standardized scripts and splits for 16 datasets spanning eight domains .
Outcome: The proposed benchmarks show that they struggle to achieve satisfactory performance.
Few-Shot Document-Level Event Argument Extraction (2023.acl-long)

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Challenge: Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level.
Approach: They propose a Few-Shot Document-Level Event Argument Extraction benchmark to capture event arguments that actually spread across sentences in documents.
Outcome: The proposed task is very challenging with low performance and limited learning process . argument extraction depends on context from multiple sentences and learning process limited to very few examples .
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 .
ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement (2024.findings-acl)

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Challenge: Structural extraction of events within discourse is critical for event-centric understanding . document-level EAE focuses on arguments that are scattered across an entire document . ULTRA is a hierarchical framework that extracts event arguments more cost-effectively .
Approach: They propose a hierarchical framework that extracts event arguments more cost-effectively . ULTRA sequentially reads text chunks of a document to generate a candidate argument set . they propose to use a supervised model to find the exact boundary of an argument .
Outcome: The proposed framework outperforms strong models and ChatGPT by 9.8% when evaluated by Exact Match (EM).
OEE-CFC: A Dataset for Open Event Extraction from Chinese Financial Commentary (2024.findings-emnlp)

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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.
Outcome: The proposed corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments.
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.
Outcome: The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research.

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