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    <abstract>Current summarization systems only produce plain, factual headlines, far from the practical needs for the exposure and memorableness of the articles. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), thus attracting more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates stylistic 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 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 outperforms the state-of-the-art summarization model by 9.68%, even outperforming human-written references.</abstract>
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%0 Conference Proceedings
%T Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
%A Jin, Di
%A Jin, Zhijing
%A Zhou, Joey Tianyi
%A Orii, Lisa
%A Szolovits, Peter
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F jin-etal-2020-hooks
%X Current summarization systems only produce plain, factual headlines, far from the practical needs for the exposure and memorableness of the articles. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), thus attracting more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates stylistic 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 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 outperforms the state-of-the-art summarization model by 9.68%, even outperforming human-written references.
%R 10.18653/v1/2020.acl-main.456
%U https://aclanthology.org/2020.acl-main.456/
%U https://doi.org/10.18653/v1/2020.acl-main.456
%P 5082-5093

Markdown (Informal)

[Hooks in the Headline: Learning to Generate Headlines with Controlled Styles](https://aclanthology.org/2020.acl-main.456/) (Jin et al., ACL 2020)

• Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (Jin et al., ACL 2020)

ACL

• Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, and Peter Szolovits. 2020. Hooks in the Headline: Learning to Generate Headlines with Controlled Styles. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5082–5093, Online. Association for Computational Linguistics.