mlsanityData to 7 Oct 2026

This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation

arXiv 1906.03497 ↗First posted 8 Jun 2019cs.CLcited by 58

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Abstract (arXiv)

Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email's content. In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body. We create the first dataset for this task and find that email subject line generation favor extremely abstractive summary which differentiates it from news headline generation or news single document summarization. We then develop a novel deep learning method and compare it to several baselines as well as recent state-of-the-art text summarization systems. We also investigate the efficacy of several automatic metrics based on correlations with human judgments and propose a new automatic evaluation metric. Our system outperforms competitive baselines given both automatic and human evaluations. To our knowledge, this is the first work to tackle the problem of effective email subject line generation.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2019.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 13; 2024: 13; 2025: 9; 2026: 4.

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  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  2. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  3. 2021Muppet: Massive Multi-task Representations with Pre-Finetuningcited by 192
  4. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  5. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  6. 2023LM-Polygraph: Uncertainty Estimation for Language Modelscited by 130
  7. 2021The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generationcited by 96
  8. 2022Language Models are General-Purpose Interfacescited by 96
  9. 2023UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluationcited by 74
  10. 2023RewriteLM: An Instruction-Tuned Large Language Model for Text Rewritingcited by 67
  11. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63
  12. 2023Learning to Retrieve In-Context Examples for Large Language Modelscited by 60
38 more
  1. 2025A Survey of Uncertainty Estimation Methods on Large Language Modelscited by 57
  2. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  3. 2021AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationcited by 51
  4. 2021A Survey on Dialogue Summarization: Recent Advances and New Frontierscited by 51
  5. 2023Adapting Large Language Models to Domains via Reading Comprehensioncited by 48
  6. 2024C-RAG: Certified Generation Risks for Retrieval-Augmented Language Modelscited by 27
  7. 2025Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similaritycited by 26
  8. 2022Targeted Phishing Campaigns using Large Scale Language Modelscited by 22
  9. 2024Data Contamination Report from the 2024 CONDA Shared Taskcited by 19
  10. 2023Towards Better Evaluation of Instruction-Following: A Case-Study in Summarizationcited by 18
  11. 2025From Selection to Generation: A Survey of LLM-based Active Learningcited by 18
  12. 2022UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarizationcited by 15
  13. 2021Meta-Transfer Learning for Low-Resource Abstractive Summarizationcited by 14
  14. 2022Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Expertscited by 12
  15. 2021Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescuecited by 11
  16. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
  17. 2024Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputscited by 11
  18. 2023Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?cited by 9
  19. 2024Turning Generative Models Degenerate: The Power of Data Poisoning Attackscited by 9
  20. 2025Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utilitycited by 9
  21. 2024Se2: Sequential Example Selection for In-Context Learningcited by 8
  22. 2024Flexible and Adaptable Summarization via Expertise Separationcited by 7
  23. 2023Active Learning for Abstractive Text Summarizationcited by 6
  24. 2024Language Model-Driven Data Pruning Enables Efficient Active Learningcited by 6
  25. 2023Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learningcited by 5
  26. 2025Learning to Select In-Context Demonstration Preferred by Large Language Modelcited by 5
  27. 2023A comprehensive review of automatic text summarization techniques: method, data, evaluation and codingcited by 4
  28. 2024Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarizationcited by 4
  29. 2022Analyzing Multi-Task Learning for Abstractive Text Summarizationcited by 3
  30. 2022Meeting Summarization: A Survey of the State of the Artcited by 3
  31. 2023ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalizationcited by 3
  32. 2024Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?cited by 3
  33. 2024The State and Fate of Summarization Datasets: A Surveycited by 3
  34. 2025DUAL: Diversity and Uncertainty Active Learning for Text Summarizationcited by 3
  35. 2022KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLPcited by 1
  36. 2022Swing Distillation: A Privacy-Preserving Knowledge Distillation Frameworkcited by 1
  37. 2025D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Modelcited by 1
  38. 2026From "Help" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applicationscited by 1

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