HonestBait: Forward References for Attractive but Faithful Headline
  Generation
Chih-Yao Chen Dennis Wu Lun-Wei Ku
Current methods for generating attractive headlines often learn directly from
data, which bases attractiveness on the number of user clicks and views.
Although clicks or views do reflect user interest, they can fail to reveal how
much interest is raised by the writing style and how much is due to the event
or topic itself. Also, such approaches can lead to harmful inventions by
over-exaggerating the content, aggravating the spread of false information. In
this work, we propose HonestBait, a novel framework for solving these issues
from another aspect: generating headlines using forward references (FRs), a
writing technique often used for clickbait. A self-verification process is
included during training to avoid spurious inventions. We begin with a
preliminary user study to understand how FRs affect user interest, after which
we present PANCO1, an innovative dataset containing pairs of fake news with
verified news for attractive but faithful news headline generation. Automatic
metrics and human evaluations show that our framework yields more attractive
results (+11.25% compared to human-written verified news headlines) while
maintaining high veracity, which helps promote real information to fight
against fake news.