Towards Topic-Aware Slide Generation For Academic Papers With Unsupervised Mutual Learning
Authors: Da-Wei Li, Danqing Huang, Tingting Ma, Chin-Yew Lin13243-13251
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Collaboration | 1 School of Software and Microelectronics, Peking University 2 Microsoft Research Asia 3 Harbin Institute of Technology |
| Pseudocode | Yes | Algorithm 1 Training paradigm based on mutual learning |
| Open Source Code | Yes | 2Our annotation and code can be found at https://github.com/daviddwlee84/Topic Aware Paper Slide Generation |
| Open Datasets | Yes | We use the ACL Anthology Reference Corpus (Bird et al. 2008) as the unlabeled corpus of papers for our unsupervised learning. |
| Dataset Splits | No | The paper describes the creation of a 'labeled test set' by annotating 100 papers, but does not specify train/validation/test splits for the model's training, nor does it explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and models like GRU, GloVe, Adam, and BERT-QA, but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | The word embedding matrix was initialized using pre-trained 50-dimension Glo Ve vectors... We use Adam (Kingma and Ba 2015) as our optimizing algorithm. The learning rate for Adam optimizer α is set to 0.001. We use dropout (Srivastava et al. 2014) as regularization with probability p = 0.3 after the sentence level encoder and p = 0.2 after the document level encoder. The training process stops when the loss of two classifiers converges. Maximum training epochs are set to 20. |