DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents

TL;DR

This paper introduces DOC2PPT, a novel end-to-end method for automatically generating presentation slides from scientific documents, combining summarization, retrieval, and layout prediction.

Contribution

It presents a hierarchical sequence-to-sequence model that leverages document structure and includes paraphrasing and layout modules for slide generation, along with a new dataset.

Findings

Outperforms strong baselines in slide quality

Produces slides with rich content and aligned imagery

Demonstrates effectiveness of hierarchical modeling

Abstract

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.