  In this paper, we present the main findings and compare the results of
SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The
goal of this shared task is to design automatic methods for emphasis selection,
i.e. choosing candidates for emphasis in textual content to enable automated
design assistance in authoring. The main focus is on short text instances for
social media, with a variety of examples, from social media posts to
inspirational quotes. Participants were asked to model emphasis using plain
text with no additional context from the user or other design considerations.
SemEval-2020 Emphasis Selection shared task attracted 197 participants in the
early phase and a total of 31 teams made submissions to this task. The
highest-ranked submission achieved 0.823 Matchm score. The analysis of systems
submitted to the task indicates that BERT and RoBERTa were the most common
choice of pre-trained models used, and part of speech tag (POS) was the most
useful feature. Full results can be found on the task's website.
