Browse State-of-the-Art › Document-level Closed Information Extraction
Document-level Closed Information Extraction
2 papers with code · 3 benchmarks · 3 datasets archive 2025-07-28
Document-level closed information extraction (DocIE) is a subtask of information extraction that seeks to extract a set of triplets, or facts, of the form (subject, relation, object) from unstructured texts that are fully linked to a reference knowledge base, i.e., consistent with a predefined set of entities and relations from a knowledge base. DocIE entails tasks such as mention detection, entity typing, named entity recognition, entity disambiguation, entity linking, coreference resolution, and document-level relation extraction. DocIE is more challenging than sentence-level closed information extraction as it involves capturing long-range dependencies effectively to extract relations between entities that are further apart from each other in the text. Another difference is that DocIE necessitates a coreference resolution stage to group all the different mentions in the document referring to the same entity. DocIE is crucial for applications such as knowledge graph construction, question answering, knowledge discovery, or text summarization.
Source: REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
3 leaderboard tables shown for this task, 3 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.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| DocRED (1 row) | REXEL | REXEL: An End-to-end Model for Document-Level Relation Extraction... | code | Syntology ran 3 of 3 samples · 0 unverified | Compare |
| DWIE (1 row) | REXEL | REXEL: An End-to-end Model for Document-Level Relation Extraction... | code | Syntology ran 3 of 3 samples · 0 unverified | Compare |
| DocRED-IE (1 row) | REXEL | REXEL: An End-to-end Model for Document-Level Relation Extraction... | code | Syntology ran 3 of 3 samples · 0 unverified | Compare |
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
3 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
2 shown of 2 papers with code (2 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.
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31 Jul 2024 2 repositories listedEntity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications.
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19 Apr 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverifiedExtracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
Syntology lines on 1 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.
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