| Challenge: | Existing methods for document-level relation extraction are incomplete and lack anaphor for identifying relations between entities. |
| Approach: | They propose an Anaphor-Assisted (AA) framework for document-level relation extraction . they use a document or sentences as intermediate nodes to model cross-sentence entity interactions . |
| Outcome: | The proposed framework achieves state-of-the-art on the widely-used datasets. |
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025.findings-acl)
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| Challenge: | Existing document-level relation extraction models focus on individual entity pairs, limiting their ability to handle complex reasoning tasks. |
| Approach: | They propose a document-level relation extraction framework based on global relations and entity pair reasoning that captures fine-grained interactions between entity pairs. |
| Outcome: | The proposed framework outperforms existing models on widely-used datasets. |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
| Approach: | They propose a document-level relation extraction framework that captures and exploits instructive information by adding extra syntactic information into text representations. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs. |
| Approach: | They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction. |
| Outcome: | The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models. |
A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
| Outcome: | The proposed model surpasses existing methods by a large margin and achieves state-of-the-art results on a document-level relation extraction dataset. |
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (D19-1)
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| Challenge: | Existing approaches to document-level relation extraction use nodes and edges as relations between nodes. |
| Approach: | They propose an edge-oriented graph neural model for document-level relation extraction that uses different types of nodes and edges to create a document-based graph. |
| Outcome: | The proposed model can learn intra- and inter-sentence relations using multi-instance learning internally. |
Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction (2023.eacl-main)
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| Challenge: | Document-level relation extraction (DocRE) predicts relations for entity pairs relying on context-dependent reasoning . a large number of annotation errors can make it difficult to distinguish large semantically close relations . |
| Approach: | They propose a loss function to improve discriminability and robustness for DocRE . they also propose supervised contrastive learning and negative label sampling strategy . |
| Outcome: | The proposed method achieves state-of-the-art results on the DocRED dataset and its recently cleaned version. |
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing (2022.naacl-main)
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| Challenge: | Document-level relation extraction models are not robust and exhibit bizarre behaviors when non-evidence sentences are removed. |
| Approach: | They propose a document-level relation extraction framework that uses a sentence importance score and a focusing loss to encourage DocRE models to focus on evidence sentences. |
| Outcome: | The proposed framework improves overall performance and makes DocRE models more robust. |
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
| Approach: | They propose an automated annotation method that integrates an LLM and a natural language inference module to generate relation triples. |
| Outcome: | The proposed method can extract relations from document-level relation datasets with minimal human effort. |
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction (2023.eacl-main)
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| Challenge: | Document-level relation extraction (DocRE) is a task of identifying relations between entities in a document. evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. |
| Approach: | They propose a memory-efficient approach that uses evidence as the supervisory signal . they propose er self-training to learn ER from automatically-generated evidence . |
| Outcome: | The proposed method exhibits state-of-the-art performance on the DocRED benchmark . it uses evidence as the supervisory signal and self-trains on massive data without annotations . |