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

By Rui Zhang, Joel R. Tetreault. 2019

85 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Finetuned Language Models Are Zero-Shot Learners (2021, neutral): “AESLC (Zhang & Tetreault, 2019) 2.”
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (2019, background): “, 2019), instructions (Koupaee and Wang, 2018), emails (Zhang and Tetreault, 2019), patents ar X iv :1 91 2.”
Few-Shot Text Generation with Natural Language Instructions (2020, methodology): “We include the following tasks: • AESLC (Zhang and Tetreault, 2019): Given an email body, predict the title of the email.”
Language Models are General-Purpose Interfaces (2022, background): “• Summarization is to generate a summary of a given passage: AESLC (Zhang and Tetreault, 2019), SamSum (Gliwa et al., 2019), XSum (Narayan et al., 2018).”
Few-Shot Text Generation with Pattern-Exploiting Training (2020, background): “Speciﬁcally, we consider the following datasets: • AESLC (Zhang and Tetreault, 2019): Given an email body, the title of the email must be predicted.”