D2S: Document-to-Slide Generation Via Query-Based Text Summarization

Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy X. R. Wang


Abstract
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.
Anthology ID:
2021.naacl-main.111
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1405–1418
Language:
URL:
https://aclanthology.org/2021.naacl-main.111
DOI:
10.18653/v1/2021.naacl-main.111
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.111.pdf
Code
 IBM/document2slides
Data
SciDuetELI5Natural Questions