Towards Topic-Aware Slide Generation For Academic Papers With Unsupervised Mutual Learning


  • Da-Wei Li School of Software and Microelectronics, Peking University
  • Danqing Huang Microsoft Research Asia
  • Tingting Ma Harbin Institute of Technology
  • Chin-Yew Lin Microsoft Research Asia





Slides are commonly used to present information and tell stories. In academic and research communities, slides are typically used to summarize findings in accepted papers for presentation in meetings and conferences. These slides for academic papers usually contain common and essential topics such as major contributions, model design, experiment details and future work. In this paper, we aim to automatically generate slides for academic papers. We first conducted an in-depth analysis of how humans create slides. We then mined frequently used slide topics. Given a topic, our approach extracts relevant sentences in the paper to provide the draft slides. Due to the lack of labeling data, we integrate prior knowledge of ground truth sentences into a log-linear model to create an initial pseudo-target distribution. Two sentence extractors are learned collaboratively and bootstrap the performance of each other. Evaluation results on a labeled test set show that our model can extract more relevant sentences than baseline methods. Human evaluation also shows slides generated by our model can serve as a good basis for preparing the final presentations.




How to Cite

Li, D.-W., Huang, D., Ma, T., & Lin, C.-Y. (2021). Towards Topic-Aware Slide Generation For Academic Papers With Unsupervised Mutual Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15), 13243-13251.



AAAI Technical Track on Speech and Natural Language Processing II