DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents
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Abstract (arXiv)
Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner. Can machines learn to emulate this laborious process? We present a novel task and approach for document-to-slide generation. Solving this involves document summarization, image and text retrieval, slide structure and layout prediction to arrange key elements in a form suitable for presentation. We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner. Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides. To help accelerate research in this domain, we release a dataset 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.
Key facts. Cited by 70 papers in the map (data through 14 Oct 2026). Builds on 49 of its 72 references that are in the map. First posted on arXiv 28 Jan 2021.
Builds on
49 of 72 references.
References · 49
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Citing papers per year: 2021: 1; 2022: 0; 2023: 9; 2024: 9; 2025: 22; 2026: 29.
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