Text Summarization is a popular task and an active area of research for the Natural Language Processing community. It requires accounting for long input texts, a characteristic which poses computational challenges for neural models. Moreover, real-world documents come in a variety of complex, visually-rich, layouts. This information is of great relevance, whether to highlight salient content or to encode long-range interactions between textual passages. Yet, all publicly available summarization datasets only provide plain text content. To facilitate research on how to exploit visual/layout information to better capture longrange dependencies in summarization models, we present LoRaLay, a collection of datasets for long-range summarization with accompanying visual/layout information. We extend existing and popular English datasets (arXiv and PubMed) with visual/layout information and propose four novel datasets-consistently built from scholar resources-covering French, Spanish, Portuguese, and Korean languages. Further, we propose new baselines merging layout-aware and long-range models-two orthogonal approaches-and obtain state-of-theart results, showing the importance of combining both lines of research.