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

Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, Peter Szolovits


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.
Anthology ID:
2020.acl-main.456
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5082–5093
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.456
DOI:
10.18653/v1/2020.acl-main.456
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.456.pdf
Video:
 http://slideslive.com/38929352