Papers › SPECTER: Document-level Representation Learning using Citation-informed Transformers
SPECTER: Document-level Representation Learning using Citation-informed Transformers
Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld
Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.
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58111a8f1b8f5e9e · report
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Classification | SciDocs (MAG) | SPECTER | F1 (micro) | 82.0 | #1 of 2 | Archive leaderboard | report |
| Document Classification | SciDocs (MeSH) | SPECTER | F1 (micro) | 86.4 | #2 of 2 | Archive leaderboard | report |
| Representation Learning | SciDocs | SPECTER | Avg. | 80.0 | #2 of 7 | Archive leaderboard | report |
| Representation Learning | SciDocs | Citeomatic | Avg. | 76.0 | #3 of 7 | Archive leaderboard | report |
| Representation Learning | SciDocs | SciBERT | Avg. | 59.6 | #5 of 7 | 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
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