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.

该研究提出并研究了电子邮件主题行生成的主题，并创建了该任务的第一个数据集。他们发现电子邮件主题行生成偏爱极度抽象的摘要，并开发了一种新颖的深度学习方法来解决这个问题，并将其与几个基础模型和最新的文本摘要系统进行了比较。研究人员还调查了几种基于人类判断的自动度量标准的效力，并提出了一种新的自动评估指标。该系统在自动和人类评估中均优于竞争基线。据我们所知，这是解决有效电子邮件主题行生成问题的首个工作。

这封电子邮件可能会挽救您的生命：介绍电子邮件主题行生成的任务