  Presentation slides have become a common addition to the teaching material.
Emphasizing strong leading words in presentation slides can allow the audience
to direct the eye to certain focal points instead of reading the entire slide,
retaining the attention to the speaker during the presentation. Despite a large
volume of studies on automatic slide generation, few studies have addressed the
automation of design assistance during the creation process. Motivated by this
demand, we study the problem of Emphasis Selection (ES) in presentation slides,
i.e., choosing candidates for emphasis, by introducing a new dataset containing
presentation slides with a wide variety of topics, each is annotated with
emphasis words in a crowdsourced setting. We evaluate a range of
state-of-the-art models on this novel dataset by organizing a shared task and
inviting multiple researchers to model emphasis in this new domain. We present
the main findings and compare the results of these models, and by examining the
challenges of the dataset, we provide different analysis components.
