Challenge: Defined event hoppers for the DEFT Rich Entities, Relations and Events (Rich ERE) annotation task.
Approach: They propose an approach for cross-document, cross-lingual event coreference for the DEFT Rich Entities, Relations and Events (Rich ERE) annotation task.
Outcome: The proposed approach is based on the definition of event hoppers for the DEFT rich entities, relations, events and their attributes . it yields 389 cross-document event hoppings in 505 documents in three languages .

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Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)

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Challenge: Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies.
Approach: They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents.
Outcome: The proposed model outperforms baseline models on English and Chinese datasets by large margins.
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia (2021.naacl-main)

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Challenge: Existing datasets for cross-document event coreference resolution are limited and small . authors present a method for identifying clusters of text mentions that refer to the same event .
Approach: They propose a method for generating a large-scale Wikipedia event coreference dataset . they use a generic approach that adapts state-of-the-art models to the cross-document setting .
Outcome: The proposed method outperforms existing models and can be applied to other languages.
Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing (2024.acl-short)

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Challenge: Existing methods for Cross-Document Event Coreference Resolution (CDEC) are biased towards lexical similarities, limiting a crucial avenue of research in event comprehension.
Approach: They propose a lexically rich variant of Event Coref Bank Plus (ECB+) for CDEC on symbolic and metaphoric language.
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Cross-document Event Coreference Search: Task, Dataset and Modeling (2022.emnlp-main)

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Challenge: Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection.
Approach: They propose a cross-document coreference search task that searches for all coreferring mentions for a query event in a large document collection.
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Beyond Benchmarks: Building a Richer Cross-Document Event Coreference Dataset with Decontextualization (2025.naacl-long)

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Challenge: Existing datasets for Cross-Document Event Coreference (CDEC) are small and lacking diversity.
Approach: They propose a new approach leveraging large language models to decontextualize event mentions by simplifying the document-level annotation task to sentence pairs with enriched context.
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MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)

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Challenge: Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English.
Approach: They propose a multiligual dataset that manually annotates documents for event mentions and coreference in five languages.
Outcome: The proposed dataset annotates documents for event mentions and coreference in five languages . the dataset fetches related news articles from the google search engine to increase the number of non-singleton clusters .
Linear Cross-document Event Coreference Resolution with X-AMR (2024.lrec-main)

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Challenge: Event Coreference Resolution (ECR) is expensive both for automated systems and manual annotations.
Approach: They propose a graphical representation of events anchored around individual mentions using a cross-document version of Abstract Meaning Representation.
Outcome: The proposed model is anchored around individual mentions using a cross-document version of Abstract Meaning Representation.
Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)

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Challenge: Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge.
Approach: They propose a model that learns and integrates multiple representations from event alone and event pair on the basis of event but not entity as before.
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Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)

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Challenge: Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts.
Approach: They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM.
Outcome: The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios.
The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models (2023.findings-acl)

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Challenge: et al., 2023) show that entity coreference resolution is improved when events bring about changes in entities that are not reflected in text mentions.
Approach: They propose to perform transformation-based entity linking prior to coreference relation identification to improve entity coreference.
Outcome: The proposed model improves coreference resolution of entities mentioned under a process-oriented model of events.

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