  Text Summarization is a popular task and an active area of research for the
Natural Language Processing community. By definition, it requires to account
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 long-range 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 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-the-art results, showing the importance of combining both lines
of research.
