Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, Peter Szolovits · 2020
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-ofthe-art summarization model by 9.68%, and even outperforms human-written references. 1