  Crises such as the COVID-19 pandemic continuously threaten our world and
emotionally affect billions of people worldwide in distinct ways. Understanding
the triggers leading to people's emotions is of crucial importance. Social
media posts can be a good source of such analysis, yet these texts tend to be
charged with multiple emotions, with triggers scattering across multiple
sentences. This paper takes a novel angle, namely, emotion detection and
trigger summarization, aiming to both detect perceived emotions in text, and
summarize events and their appraisals that trigger each emotion. To support
this goal, we introduce CovidET (Emotions and their Triggers during Covid-19),
a dataset of ~1,900 English Reddit posts related to COVID-19, which contains
manual annotations of perceived emotions and abstractive summaries of their
triggers described in the post. We develop strong baselines to jointly detect
emotions and summarize emotion triggers. Our analyses show that CovidET
presents new challenges in emotion-specific summarization, as well as
multi-emotion detection in long social media posts.
