Document-level Relation Extraction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 66 papers with code (106 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• DocRED: A Large-Scale Document-Level Relation Extraction Dataset
  14 Jun 2019 4 repositories listed
  Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for…
• Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
  25 May 2022 3 repositories listed
  We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.
• Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
  20 Feb 2021 3 repositories listed
  Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
• Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion
  1 Apr 2025 2 repositories listed
  Further, in order to effectively solve the multi-label relation prediction problem in the refinement process, we propose a probability fusion method, ensuring and enhancing fusion predictions by maintaining a balance…
• AutoRE: Document-Level Relation Extraction with Large Language Models
  21 Mar 2024 2 repositories listed Syntology ran 4 of 8 samples · 4 unverified · 8 pointer-only (licence)
  Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extraction (IE) purposes, including Relation…
• A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes
  13 Apr 2022 2 repositories listed
  We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models.
• A sequence-to-sequence approach for document-level relation extraction
  3 Apr 2022 2 repositories listed
  In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of…
• Document-level Relation Extraction as Semantic Segmentation
  7 Jun 2021 2 repositories listed
  Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
• Discriminative Reasoning for Document-level Relation Extraction
  3 Jun 2021 2 repositories listed Syntology ran 8 of 10 samples · 2 unverified
  Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.
• Double Graph Based Reasoning for Document-level Relation Extraction
  29 Sep 2020 2 repositories listed
  Document-level relation extraction aims to extract relations among entities within a document.
• Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
  13 May 2020 2 repositories listed Syntology ran 5 of 7 samples · 2 unverified · 6 pointer-only (licence)
  Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.
• Enhancing Biomedical Relation Extraction with Directionality
  23 Jan 2025 1 repository listed
  Nonetheless, its annotations lack directionality (subject/object) for the entity roles, essential for studying complex biological networks.
• VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction
  18 Dec 2024 1 repository listed
  Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document.
• TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction
  1 Oct 2024 1 repository listed
  Few-shot document-level relation extraction suffers from poor performance due to the challenging cross-domain transferability of NOTA (none-of-the-above) relation representation.
• LLM with Relation Classifier for Document-Level Relation Extraction
  25 Aug 2024 1 repository listed
  Large language models (LLMs) have created a new paradigm for natural language processing.
• Consistent Document-Level Relation Extraction via Counterfactuals
  9 Jul 2024 1 repository listed
  We first demonstrate that models trained on factual data exhibit inconsistent behavior: while they accurately extract triples from factual data, they fail to extract the same triples after counterfactual modification.
• On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations
  11 Jun 2024 1 repository listed
  To this end, we systematically investigate the robustness of DocRE models to entity name variations in this work.
• TTM-RE: Memory-Augmented Document-Level Relation Extraction
  9 Jun 2024 1 repository listed Syntology ran 3 of 5 samples · 2 unverified
  Document-level relation extraction aims to categorize the association between any two entities within a document.
• REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
  19 Apr 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
• FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction
  4 Mar 2024 1 repository listed
  State-of-the-art DocRE methods use a graph structure to connect entities across the document to capture dependency syntax information.
• Extracting Polymer Nanocomposite Samples from Full-Length Documents
  1 Mar 2024 1 repository listed
  This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers.
• Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
  22 Jan 2024 1 repository listed
  Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input.
• Adaptive Hinge Balance Loss for Document-Level Relation Extraction
  6 Dec 2023 1 repository listed
  In this paper, we propose to downweight the easy negatives by utilizing a distance between the classification threshold and the predicted score of each relation.
• Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
  13 Nov 2023 1 repository listed
  Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and generating interpretable document…
• Anaphor Assisted Document-Level Relation Extraction
  28 Oct 2023 1 repository listed
  Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities.
• RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
  24 Oct 2023 1 repository listed
  However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that…
• Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models
  17 Oct 2023 1 repository listed
  Document-level relation extraction aims at inferring structured human knowledge from textual documents.
• Document-Level Relation Extraction with Relation Correlation Enhancement
  6 Oct 2023 1 repository listed
  Document-level relation extraction (DocRE) is a task that focuses on identifying relations between entities within a document.
• PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration
  25 Sep 2023 1 repository listed
  Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document.
• HistRED: A Historical Document-Level Relation Extraction Dataset
  10 Jul 2023 1 repository listed
  To demonstrate the usefulness of our dataset, we propose a bilingual RE model that leverages both Korean and Hanja contexts to predict relations between entities.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.