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.

本文提出了一个名为 SciDuet 的新数据集，其中包括最近年份 NLP 和 ML 会议上的文献和其对应的幻灯片，同时介绍了一个名为 D2S 的新系统，该系统采用两步法来处理文档到幻灯片的任务，评估表明长型问答在 ROUGE 自动度量和人工评估方面优于基线摘要。