DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation


  • Yifan Wang Peking University
  • Yiping Song National University of Defense Technology
  • Shuai Li Peking University
  • Chaoran Cheng Peking University
  • Wei Ju Peking University
  • Ming Zhang Peking University
  • Sheng Wang Paul G. Allen School of Computer Science, University of Washington




Speech & Natural Language Processing (SNLP), Data Mining & Knowledge Management (DMKM)


Citing and describing related literature are crucial to scientific writing. Many existing approaches show encouraging performance in citation recommendation, but are unable to accomplish the more challenging and onerous task of citation text generation. In this paper, we propose a novel disentangled representation based model DisenCite to automatically generate the citation text through integrating paper text and citation graph. A key novelty of our method compared with existing approaches is to generate context-specific citation text, empowering the generation of different types of citations for the same paper. In particular, we first build and make available a graph enhanced contextual citation dataset (GCite) with 25K edges in different types characterized by citation contained sections over 4.8K research papers. Based on this dataset, we encode each paper according to both textual contexts and structure information in the heterogeneous citation graph. The resulted paper representations are then disentangled by the mutual information regularization between this paper and its neighbors in graph. Extensive experiments demonstrate the superior performance of our method comparing to state-of-the-art approaches. We further conduct ablation and case studies to reassure that the improvement of our method comes from generating the context-specific citation through incorporating the citation graph.




How to Cite

Wang, Y., Song, Y., Li, S., Cheng, C., Ju, W., Zhang, M., & Wang, S. (2022). DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11449-11458. https://doi.org/10.1609/aaai.v36i10.21397



AAAI Technical Track on Speech and Natural Language Processing