The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attractive content but also follow an eye-catching written style. In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. Concretely, we first devise a disentanglement module to divide the style and content of an attractive prototype headline into latent spaces, with two auxiliary constraints to ensure the two spaces are indeed disentangled. The latent content information is then used to further polish the document representation and help capture the salient part. Finally, the generator takes the polished document as input to generate headline under the guidance of the attractive style. Extensive experiments on the public Kuaibao dataset show that DAHG achieves state-ofthe-art performance. Human evaluation also demonstrates that DAHG triggers 22% more clicks than existing models.

Authors

• Mingzhe Li Ant Group
• Xiuying Chen Computational Bioscience Research Center, KAUST
• Min Yang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China
• Shen Gao Shandong University
• Dongyan Zhao Wangxuan Institute of Computer Technology, Peking University National Key Laboratory of General Artificial Intelligence
• Rui Yan

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