  Posts, as important containers of user-generated-content pieces on social
media, are of tremendous social influence and commercial value. As an integral
components of a post, the headline has a decisive contribution to the post's
popularity. However, current mainstream method for headline generation is still
manually writing, which is unstable and requires extensive human effort. This
drives us to explore a novel research question: Can we automate the generation
of popular headlines on social media? We collect more than 1 million posts of
42,447 celebrities from public data of Xiaohongshu, which is a well-known
social media platform in China. We then conduct careful observations on the
headlines of these posts. Observation results demonstrate that trends and
personal styles are widespread in headlines on social medias and have
significant contribution to posts's popularity. Motivated by these insights, we
present MEBART, which combines Multiple preference-Extractors with
Bidirectional and Auto-Regressive Transformers (BART), capturing trends and
personal styles to generate popular headlines on social medias. We perform
extensive experiments on real-world datasets and achieve state-of-the-art
performance compared with several advanced baselines. In addition, ablation and
case studies demonstrate that MEBART advances in capturing trends and personal
styles.
