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

Unsupervised Extractive Summarization of Emotion Triggers