Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents
Authors: Tsu-Jui Fu, William Yang Wang, Daniel McDuff, Yale Song634-642
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We release a dataset of about 6K paired documents and slide decks used in our experiments. We show that our approach outperforms strong baselines and produces slides with rich content and aligned imagery. |
| Researcher Affiliation | Collaboration | Tsu-Jui Fu1, William Yang Wang1, Daniel Mc Duff2, Yale Song2 1 UC Santa Barbara 2 Microsoft Research |
| Pseudocode | No | The paper includes architectural diagrams and mathematical equations but does not present any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Project webpage: https://doc2ppt.github.io/ |
| Open Datasets | Yes | To help accelerate research in this domain, we release a dataset of about 6K paired documents and slide decks used in our experiments. Project webpage: https://doc2ppt.github.io/ |
| Dataset Splits | Yes | Table 1: Descriptive statistics of our dataset. We report both the total count and the average number (in parenthesis). Train / Val / Test: CV 2,073 / 265 / 262, NLP 741 / 93 / 97, ML 1,872 / 234 / 236, Total 4,686 / 592 / 595 |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only general statements like 'We train our network end-to-end'. |
| Software Dependencies | No | The paper mentions software components like RoBERTa, ResNet-152, Bi-GRU, Seq2Seq, and ADAM, but it does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | For the DR, we use a Bi-GRU with 1,024 hidden units and set the MLPs to output 1,024-dimensional embeddings. Each layer of the PT is based on a 256-unit GRU. The PAR is designed as Seq2Seq (Bahdanau, Cho, and Bengio 2015) with 512-unit GRU. We train our network end-to-end using ADAM (Diederik P. Kingma 2014) withlearning rate 3e-4. We tune the two hyper-parameters θR and θA via cross-validation (we set θR = 0.8, θA = 0.9). |