# CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization

arXiv: https://arxiv.org/abs/2301.11223 · HTML: https://mlsanity.com/paper/2301.11223/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2023.

## Built on by (most cited first, 9 of 9)

- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [When Large Language Models Meet Citation: A Survey](https://mlsanity.com/paper/2309.09727/) (2023)
- [A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models](https://mlsanity.com/paper/2304.08763/) (2023)
- [KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model](https://mlsanity.com/paper/2502.20350/) (2025)
- [Enhancing Abstractive Summarization of Scientific Papers Using Structure Information](https://mlsanity.com/paper/2505.14179/) (2025)
- [Graph Contrastive Topic Model](https://mlsanity.com/paper/2307.02078/) (2023)
- [FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization](https://mlsanity.com/paper/2510.11250/) (2025)
- [Preface to the Special Issue of the TAL Journal on Scholarly Document Processing](https://mlsanity.com/paper/2506.03587/) (2025)
- [Struc-EMB: The Potential of Structure-Aware Encoding in Language Embeddings](https://mlsanity.com/paper/2510.08774/) (2025)

## Abstract (arXiv)

Citation graphs can be helpful in generating high-quality summaries of scientific papers, where references of a scientific paper and their correlations can provide additional knowledge for contextualising its background and main contributions. Despite the promising contributions of citation graphs, it is still challenging to incorporate them into summarization tasks. This is due to the difficulty of accurately identifying and leveraging relevant content in references for a source paper, as well as capturing their correlations of different intensities. Existing methods either ignore references or utilize only abstracts indiscriminately from them, failing to tackle the challenge mentioned above. To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on citation graphs, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references. Specifically, we first build a domain-specific dataset PubMedCite with about 192K biomedical scientific papers and a large citation graph preserving 917K citation relationships between them. It is characterized by preserving the salient contents extracted from full texts of references, and the weighted correlation between the salient contents of references and the source paper. Based on it, we design a self-supervised citation-aware summarization framework (CitationSum) with graph contrastive learning, which boosts the summarization generation by efficiently fusing the salient information in references with source paper contents under the guidance of their correlations. Experimental results show that our model outperforms the state-of-the-art methods, due to efficiently leveraging the information of references and citation correlations.