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.

翻译：摘要：引文图有助于生成高质量的科学论文摘要，其中论文的参考文献及其关联能为理解论文背景和主要贡献提供额外知识。尽管引文图展现出显著潜力，将其融入摘要生成任务仍面临挑战。这主要源于难以准确识别并利用参考文献与源论文的相关内容，同时捕捉不同强度的引文关联。现有方法要么忽略参考文献，要么无差别地使用其摘要，未能解决上述难题。为此，我们提出一种基于引文图的新型引文感知科学论文摘要框架，能够准确定位并融入参考文献中的关键内容，同时捕捉源论文与参考文献之间的动态相关性。具体而言，我们首先构建了领域专属数据集PubMedCite，包含约19.2万篇生物医学科学论文及91.7万条引文关系。该数据集的特点在于保存了从参考文献全文提取的关键内容，并标注了这些关键内容与源论文之间的加权关联。基于此，我们设计了一种结合图对比学习的自监督引文感知摘要框架（CitationSum），通过关联信息引导下高效融合参考文献关键内容与源论文信息，提升摘要生成质量。实验结果表明，由于有效利用了参考文献及其引文关联信息，我们的模型性能优于现有最先进方法。