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.

LoRaLay是一个包含带有布局信息的长距离摘要的数据集合，拓展现有的英语数据集（arXiv和PubMed）并提供了四个覆盖法语、西班牙语、葡萄牙语和韩语的新数据集。在这篇文章中，我们提出了结合布局感知和长程模型的新基线，并获得了最先进的结果，这显示了在摘要模型中结合两种研究线路的重要性。

LoRaLay: 长距离和布局感知摘要的多语言和多模态数据集