SPECTER: Document-level Representation Learning using Citation-informed Transformers (2004.07180v4)

Abstract: Representation learning is a critical ingredient for natural language processing systems. Recent Transformer LLMs 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 LLM on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained LLMs, 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.

• The paper introduces a citation-informed pretraining method using a triplet-loss objective to generate cohesive document embeddings.
• It employs SciBERT fine-tuned on a citation graph and validates performance on the SciDocs benchmark across tasks like classification and recommendation.
• Empirical results show significant improvements over baselines in precision metrics for citation prediction and user activity modeling.

SPECTER: Document-level Representation Learning using Citation-informed Transformers

The paper introduces SPECTER, a novel approach for document-level representation learning tailored for scientific literature. Utilizing a Transformer model, specifically optimized on the citation graph, SPECTER aims to generate embeddings that effectively encompass the entire document, rather than focusing solely on token or sentence-level representations. This innovative method leverages citations as signals of relatedness between documents to enhance the quality of embeddings.

Key Contributions

1. Citation-Informed Pretraining: SPECTER introduces a training methodology that incorporates citation information into the Transformer model's learning process. By using a triplet-loss objective, the model learns to produce closer representations for documents that are related through citations, thereby facilitating more accurate document-level embeddings.
2. SciDocs Benchmark: To rigorously evaluate the effectiveness of document-level embeddings, the authors introduce SciDocs—a comprehensive benchmark suite encompassing seven distinct tasks. These tasks range across citation prediction, document classification, and recommendation, offering a robust framework for assessment.
3. Empirical Evaluation: The empirical results presented indicate that SPECTER significantly outperforms competitive baselines across different tasks in the SciDocs benchmark. Notably, SPECTER achieved an average performance improvement across tasks with strong scores in document classification and user activity prediction.

Methodological Insights

The paper details the implementation of SPECTER, which is built upon SciBERT—a variant of BERT tailored for scientific text. This foundation is fine-tuned using citation data, with the model pretrained on 146K query papers from the Semantic Scholar corpus. SPECTER's encoding strategy focuses on document abstracts and titles, in contrast to full-text analysis, which presents several advantages, such as scalability and applicability in cases where full text is unavailable.

Moreover, the model eschews the need for task-specific fine-tuning during downstream application, generating embeddings that can be readily used as features in various tasks, thereby streamlining its integration into diverse pipelines.

Experimentation and Evaluation

1. Classification and User Activity: In tasks like MeSH and MAG classification, SPECTER yielded F1 scores outperforming traditional models such as Doc2Vec and FastText. Additionally, in predicting patterns of user activity like co-views and co-reads, the citation-informed embeddings provided by SPECTER showed superior performance in metrics like Mean Average Precision (MAP) and Discounted Cumulative Gain (DCG).
2. Citation Prediction: SPECTER's performance in direct citation and co-citation prediction tasks surpassed baselines including SciBERT and SGC, indicating its heightened ability to encapsulate citation-based document relationships.
3. Recommendation System: The paper also shows SPECTER's embeddings improving a live recommendation system's performance metrics through online A/B testing, evidencing real-world applicability.

Implications and Future Perspectives

The implications of SPECTER's findings are notable for the AI community's effort to enhance document representation, particularly in academic and scientific information retrieval systems. The model’s capability to effectively map document relatedness without requiring extensive human-engineered features highlights its potential utility in automated mapping of scientific knowledge.

Future research could explore extending this approach to other domains where citation networks are prevalent, or incorporating additional metadata like author information and publication venues. Further, integrating alternative metrics of document relatedness and exploring multitask learning strategies for more robust representation learning could push the boundaries of this research.

Overall, SPECTER represents a meaningful progression in leveraging citation data for document-level representation learning, offering robust improvements for scientific document analysis tasks.