DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents

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, and slide structure to arrange key elements in a form suitable for presentation. We propose a hierarchical sequence-tosequence 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 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.

Authors

• Tsu-Jui Fu University of California, Santa Barbara
• William Yang Wang University of California, Santa Barbara, USA
• Daniel McDuff
• Yale Song

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