# A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1903.06464 · HTML: https://mlsanity.com/paper/1903.06464/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 29 of 29)

- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [LitSearch: A Retrieval Benchmark for Scientific Literature Search](https://mlsanity.com/paper/2407.18940/) (2024)
- [CiteME: Can Language Models Accurately Cite Scientific Claims?](https://mlsanity.com/paper/2407.12861/) (2024)
- [Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking](https://mlsanity.com/paper/2112.01206/) (2021)
- [AI Risk Management Should Incorporate Both Safety and Security](https://mlsanity.com/paper/2405.19524/) (2024)
- [Constructing Flow Graphs from Procedural Cybersecurity Texts](https://mlsanity.com/paper/2105.14357/) (2021)
- [When Large Language Models Meet Citation: A Survey](https://mlsanity.com/paper/2309.09727/) (2023)
- [CiteBART: Learning to Generate Citations for Local Citation Recommendation](https://mlsanity.com/paper/2412.17534/) (2024)
- [Graph Neural Networks: a bibliometrics overview](https://mlsanity.com/paper/2201.01188/) (2022)
- [Citation Recommendation based on Argumentative Zoning of User Queries](https://mlsanity.com/paper/2501.18292/) (2025)
- [News Article Retrieval in Context for Event-centric Narrative Creation](https://mlsanity.com/paper/2106.16053/) (2021)
- [Why Did You Not Compare With That? Identifying Papers for Use as Baselines](https://mlsanity.com/paper/2201.08089/) (2022)
- [Graph Enhanced BERT for Query Understanding](https://mlsanity.com/paper/2204.06522/) (2022)
- [ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation](https://mlsanity.com/paper/2403.08737/) (2024)
- [SymTax: Symbiotic Relationship and Taxonomy Fusion for Effective Citation Recommendation](https://mlsanity.com/paper/2406.01606/) (2024)
- [Hierarchical Memorization in Large Language Models: Evidence from Citation Generation](https://mlsanity.com/paper/2511.08877/) (2025)
- [MSME: A Multi-Stage Multi-Expert Framework for Zero-Shot Stance Detection](https://mlsanity.com/paper/2512.04492/) (2025)
- [BiblioDAP: The 1st Workshop on Bibliographic Data Analysis and Processing](https://mlsanity.com/paper/2106.12320/) (2021)
- [GraphConfRec: A Graph Neural Network-Based Conference Recommender System](https://mlsanity.com/paper/2106.12340/) (2021)
- [SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment](https://mlsanity.com/paper/2505.20103/) (2025)
- [Hallucinations in Bibliographic Recommendation: Citation Frequency as a Proxy for Training Data Redundancy](https://mlsanity.com/paper/2510.25378/) (2025)
- [Detecting and analyzing missing citations to published scientific entities](https://mlsanity.com/paper/2210.10073/) (2022)
- [A Survey on Conversational Search and Applications in Biomedicine](https://mlsanity.com/paper/2211.15328/) (2022)
- [Analyzing the Traffic of MANETs using Graph Neural Networks](https://mlsanity.com/paper/2212.08923/) (2022)
- [Enriching language models with graph-based context information to better understand textual data](https://mlsanity.com/paper/2305.11070/) (2023)
- [UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition](https://mlsanity.com/paper/2307.11170/) (2023)
- [Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild](https://mlsanity.com/paper/2603.17361/) (2026)
- [Talk2Ref: A Dataset for Reference Prediction from Scientific Talks](https://mlsanity.com/paper/2510.24478/) (2025)
- [Local Citation Recommendation Without Graph Signals](https://mlsanity.com/paper/2610.15832/) (2026)

## Abstract (arXiv)

With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, context-aware citation recommendation has been researched upon for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder, which uses Graph Convolutional Networks (GCN) layer and Bidirectional Encoder Representations from Transformers (BERT), which is a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, This dataset is the first well-organized dataset for context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision (MAP) and recall@k.