# EmailSum: Abstractive Email Thread Summarization

arXiv: https://arxiv.org/abs/2107.14691 · HTML: https://mlsanity.com/paper/2107.14691/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [Re-evaluating Evaluation in Text Summarization](https://mlsanity.com/paper/2010.07100/) (2020)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [Trivial Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1809.00357/) (2018)
- [From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information](https://mlsanity.com/paper/2005.04684/) (2020)
- [Using Summarization to Discover Argument Facets in Online Ideological Dialog](https://mlsanity.com/paper/1709.00662/) (2017)

## Built on by (most cited first, 24 of 24)

- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://mlsanity.com/paper/2205.12854/) (2022)
- [CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning](https://mlsanity.com/paper/2112.08713/) (2021)
- [Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2203.01552/) (2022)
- [VCSUM: A Versatile Chinese Meeting Summarization Dataset](https://mlsanity.com/paper/2305.05280/) (2023)
- [Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning](https://mlsanity.com/paper/2410.08014/) (2024)
- [BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation](https://mlsanity.com/paper/2110.09147/) (2021)
- [DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization](https://mlsanity.com/paper/2212.10018/) (2022)
- [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://mlsanity.com/paper/2212.01476/) (2022)
- [PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization](https://mlsanity.com/paper/2307.12371/) (2023)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users](https://mlsanity.com/paper/2310.20479/) (2023)
- [Towards Understanding Omission in Dialogue Summarization](https://mlsanity.com/paper/2211.07145/) (2022)
- [Summarizing Community-based Question-Answer Pairs](https://mlsanity.com/paper/2211.09892/) (2022)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [Responsible AI Considerations in Text Summarization Research: A Review of Current Practices](https://mlsanity.com/paper/2311.11103/) (2023)
- [Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.09319/) (2026)
- [CHOIR: A Chatbot-mediated Organizational Memory Leveraging Communication in University Research Labs](https://mlsanity.com/paper/2509.20512/) (2025)
- [Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025](https://mlsanity.com/paper/2606.02255/) (2026)
- [Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails](https://mlsanity.com/paper/2508.20328/) (2025)
- [When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models](https://mlsanity.com/paper/2602.22145/) (2026)
- [PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents](https://mlsanity.com/paper/2603.11955/) (2026)
- [ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts](https://mlsanity.com/paper/2604.17648/) (2026)
- [EmailBench: A Benchmark for Evaluating LLM Agents on Enterprise Email and Productivity Tasks](https://mlsanity.com/paper/2609.31906/) (2026)

## Abstract (arXiv)

Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency. To spur research in thread summarization, we have developed an abstractive Email Thread Summarization (EmailSum) dataset, which contains human-annotated short (<30 words) and long (<100 words) summaries of 2549 email threads (each containing 3 to 10 emails) over a wide variety of topics. We perform a comprehensive empirical study to explore different summarization techniques (including extractive and abstractive methods, single-document and hierarchical models, as well as transfer and semisupervised learning) and conduct human evaluations on both short and long summary generation tasks. Our results reveal the key challenges of current abstractive summarization models in this task, such as understanding the sender's intent and identifying the roles of sender and receiver. Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task. Hence, we emphasize the importance of human evaluation and the development of better metrics by the community. Our code and summary data have been made available at: https://github.com/ZhangShiyue/EmailSum