Challenge: a cross-document coreference annotation schema was developed to extract timelines in the clinical domain.
Approach: They propose a cross-document coreference annotation schema that is governed by schematic rules to create meaningful and consistent cross- document relations.
Outcome: The proposed approach produces an agreement score of 93.77% for identical relations between the two sets of notes.

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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.
Outcome: The proposed model integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.
xCoRe: Cross-context Coreference Resolution (2025.emnlp-main)

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Challenge: Current coreference resolution systems are limited to short-to-medium-sized documents and struggle to scale to very long documents due to architectural limitations and implied memory costs.
Approach: They propose a unified approach to coreference resolution that unifies two challenging settings . they use a pipeline that first identifies mentions, then creates clusters within individual contexts .
Outcome: The proposed model achieves state-of-the-art results on cross-document benchmarks and strong performance on long-document data while retaining top-tier results on traditional datasets.
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference (2021.emnlp-main)

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Challenge: Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications.
Approach: They propose to model entities/events in a reader’s focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters.
Outcome: The proposed model achieves state-of-the-art for events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-DDocument Coreference corpora.
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.
Data-driven Coreference-based Ontology Building (2024.findings-emnlp)

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Challenge: a new ontology is based on coreference resolution, but it is not comprehensive . a recent study found that ontologies categorize concepts into groups and arrange them in a hierarchy .
Approach: They derive coreference chains from a corpus of 30 million biomedical abstracts and construct a graph based on the string phrases within these chains.
Outcome: The proposed ontology overlaps significantly with human-authored ontologies.
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports (2022.lrec-1)

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Challenge: Oftentimes, these findings and devices are referred to multiple times in a single report and are also referred across different reports of a patient.
Approach: They propose a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient's radiology reports.
Outcome: The proposed dataset contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies.
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)

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Challenge: Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task.
Approach: They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures.
Outcome: The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus.
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.
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)

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Challenge: Existing datasets vary in definition of coreferences and are curated for linguistic experts.
Approach: They propose to use ezCoref to create a crowdsourcing-friendly coreference annotation methodology that teaches annotators only cases that are treated similarly across existing datasets.
Outcome: The proposed method reannotates 240 passages from seven existing english coreference datasets while teaching annotators only cases that are treated similarly across them.
Annotation and Evaluation of Coreference Resolution in Screenplays (2021.findings-acl)

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Challenge: Screenplays refer to characters using different names, pronouns, and nominal expressions.
Approach: They develop an automatic screenplay parser to extract structural information and design coreference rules based upon the structure of screenplays.
Outcome: The proposed model outperforms a benchmark model on the screenplay coreference resolution task.

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