Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years' NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.
@article{arxiv.2105.03664,
title = {D2S: Document-to-Slide Generation Via Query-Based Text Summarization},
author = {Edward Sun and Yufang Hou and Dakuo Wang and Yunfeng Zhang and Nancy X. R. Wang},
journal= {arXiv preprint arXiv:2105.03664},
year = {2021}
}