  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.
