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

SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media