  Understanding what leads to emotions during large-scale crises is important
as it can provide groundings for expressed emotions and subsequently improve
the understanding of ongoing disasters. Recent approaches trained supervised
models to both detect emotions and explain emotion triggers (events and
appraisals) via abstractive summarization. However, obtaining timely and
qualitative abstractive summaries is expensive and extremely time-consuming,
requiring highly-trained expert annotators. In time-sensitive, high-stake
contexts, this can block necessary responses. We instead pursue unsupervised
systems that extract triggers from text. First, we introduce CovidET-EXT,
augmenting (Zhan et al. 2022)'s abstractive dataset (in the context of the
COVID-19 crisis) with extractive triggers. Second, we develop new unsupervised
learning models that can jointly detect emotions and summarize their triggers.
Our best approach, entitled Emotion-Aware Pagerank, incorporates emotion
information from external sources combined with a language understanding
module, and outperforms strong baselines. We release our data and code at
https://github.com/tsosea2/CovidET-EXT.
