# Unsupervised Extractive Summarization of Emotion Triggers

arXiv: https://arxiv.org/abs/2306.01444 · HTML: https://mlsanity.com/paper/2306.01444/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [Text Summarization Techniques: A Brief Survey](https://mlsanity.com/paper/1707.02268/) (2017)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021)
- [Detecting Perceived Emotions in Hurricane Disasters](https://mlsanity.com/paper/2004.14299/) (2020)
- [Footwear Size Recommendation System](https://mlsanity.com/paper/1806.11423/) (2018)
- [One Embedding To Do Them All](https://mlsanity.com/paper/1906.12120/) (2019)
- [Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset](https://mlsanity.com/paper/2108.01042/) (2021)

## Built on by (most cited first, 5 of 5)

- [Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models](https://mlsanity.com/paper/2310.14389/) (2023)
- [Incremental Extractive Opinion Summarization Using Cover Trees](https://mlsanity.com/paper/2401.08047/) (2024)
- [Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion](https://mlsanity.com/paper/2311.09602/) (2023)
- [Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video](https://mlsanity.com/paper/2405.08890/) (2024)
- [MASIVE: Open-Ended Affective State Identification in English and Spanish](https://mlsanity.com/paper/2407.12196/) (2024)

## Abstract (arXiv)

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.