Automatic text summarization has experienced substantial progress in recent
years. With this progress, the question has arisen whether the types of
summaries that are typically generated by automatic summarization models align
with users' needs. Ter Hoeve et al (2020) answer this question negatively.
Amongst others, they recommend focusing on generating summaries with more
graphical elements. This is in line with what we know from the
psycholinguistics literature about how humans process text. Motivated from
these two angles, we propose a new task: summarization with graphical elements,
and we verify that these summaries are helpful for a critical mass of people.
We collect a high quality human labeled dataset to support research into the
task. We present a number of baseline methods that show that the task is
interesting and challenging. Hence, with this work we hope to inspire a new
line of research within the automatic summarization community.

自动文本摘要在近年来取得了实质性的进展。Ter Hoeve 等认为自动摘要生成的类型与用户需求不符，推荐增加更多图形元素。基于这个任务，我们提出了摘要与图形元素结合的新任务，并验证这些摘要对大众有助益。我们收集了一个高质量的人工标注数据集，提供基线模型表明该任务是有趣和具有挑战性的。希望这项工作能够在自动摘要社区中引起新的研究线路。