Hooks in the Headline: Learning to Generate Headlines with Controlled Styles

• URL: http://arxiv.org/abs/2004.01980v3
• Date: Fri, 29 May 2020 02:21:37 GMT
• Title: Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
• Authors: Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, Peter Szolovits
• Abstract summary: We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options. TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.
• Score: 69.30101340243375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from the text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.

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