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.

Similar Papers

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.
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.
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.
Approach: They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome: The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.
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.
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 .
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.
Automatic Data Acquisition for Event Coreference Resolution (2021.eacl-main)

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Challenge: lexical paraphrases and high precision rules informed by news discourse structure can be used to collect coreferential and non-coreferential event pairs from unlabeled English news articles.
Approach: They propose to use lexical paraphrases and news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles.
Outcome: The proposed model performs better than the supervised model on evaluation datasets with different event domains and text genres.
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.
Event Extraction in Video Transcripts (2022.coling-1)

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Challenge: Existing EE datasets are limited to formally written documents such as news articles or scientific papers . existing EE methods and datasets cannot be used in informal and noisy texts .
Approach: They propose to use video transcripts as a dataset for event extraction . they demonstrate that existing state-of-the-art EE methods cannot achieve adequate performance .
Outcome: The proposed dataset evaluates state-of-the-art EE methods on streamed videos on Behance . it shows that such systems cannot achieve adequate performance on the proposed dataset .

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