  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.
