Papers › Document-level Entity-based Extraction as Template Generation
Document-level Entity-based Extraction as Template Generation
Kung-Hsiang Huang, Sam Tang, Nanyun Peng
Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level EE systems build extractive models, which struggle to model long-term dependencies among entities at the document level. To address this issue, we propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE). We first formulate them as a template generation problem, allowing models to efficiently capture cross-entity dependencies, exploit label semantics, and avoid the exponential computation complexity of identifying N-ary relations. A novel cross-attention guided copy mechanism, TopK Copy, is incorporated into a pre-trained sequence-to-sequence model to enhance the capabilities of identifying key information in the input document. Experiments done on the MUC-4 and SciREX dataset show new state-of-the-art results on REE (+3.26%), binary RE (+4.8%), and 4-ary RE (+2.7%) in F1 score.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 4-ary Relation Extraction | SciREX | TempGen | Avg. F1 | 3.55 | #1 of 1 | Archive leaderboard | report |
| Binary Relation Extraction | SciREX | TempGen | Avg. F1 | 14.47 | #1 of 1 | Archive leaderboard | report |
| Role-filler Entity Extraction | MUC-4 | TempGen | Avg. F1 | 57.76 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: TopK Copy
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