  Creating a trailer requires carefully picking out and piecing together brief
enticing moments out of a longer video, making it a challenging and
time-consuming task. This requires selecting moments based on both visual and
dialogue information. We introduce a multi-modal method for predicting the
trailerness to assist editors in selecting trailer-worthy moments from
long-form videos. We present results on a newly introduced soap opera dataset,
demonstrating that predicting trailerness is a challenging task that benefits
from multi-modal information. Code is available at
https://github.com/carlobretti/cliffhanger
