Enhancing Scientific Papers Summarization with Citation Graph


  • Chenxin An Fudan University
  • Ming Zhong Fudan University
  • Yiran Chen Fudan University
  • Danqing Wang Fudan University
  • Xipeng Qiu Fudan University
  • Xuanjing Huang Fudan University






Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries.




How to Cite

An, C., Zhong, M., Chen, Y., Wang, D., Qiu, X., & Huang, X. (2021). Enhancing Scientific Papers Summarization with Citation Graph. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12498-12506. https://doi.org/10.1609/aaai.v35i14.17482



AAAI Technical Track on Speech and Natural Language Processing I