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Stylistic headline generation is the task to generate a headline that notonly summarizes the content of an article, but also reflects a desired stylethat attracts users. As style-specific article-headline pairs are scarce,previous researches focus on unsupervised approaches with a standard headlinegeneration dataset and mono-style corpora. In this work, we follow this lineand propose StyleBART, an unsupervised approach for stylistic headlinegeneration. Our method decorates the pretrained BART model with adapters thatare responsible for different styles and allows the generation of headlineswith diverse styles by simply switching the adapters. Different from previousworks, StyleBART separates the task of style learning and headline generation,making it possible to freely combine the base model and the style adaptersduring inference. We further propose an inverse paraphrasing task to enhancethe style adapters. Extensive automatic and human evaluations show thatStyleBART achieves new state-of-the-art performance in the unsupervisedstylistic headline generation task, producing high-quality headlines with thedesired style.

StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation