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

• URL: http://arxiv.org/abs/2105.03664v1
• Date: Sat, 8 May 2021 10:29:41 GMT
• Title: D2S: Document-to-Slide Generation Via Query-Based Text Summarization
• Authors: Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy X.R. Wang
• Abstract summary: We contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years' NLP and ML conferences. Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.
• Score: 27.576875048631265
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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