  With the rapid proliferation of online media sources and published news,
headlines have become increasingly important for attracting readers to news
articles, since users may be overwhelmed with the massive information. In this
paper, we generate inspired headlines that preserve the nature of news articles
and catch the eye of the reader simultaneously. The task of inspired headline
generation can be viewed as a specific form of Headline Generation (HG) task,
with the emphasis on creating an attractive headline from a given news article.
To generate inspired headlines, we propose a novel framework called
POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG).
PORL-HG exploits the extractive-abstractive architecture with 1) Popular Topic
Attention (PTA) for guiding the extractor to select the attractive sentence
from the article and 2) a popularity predictor for guiding the abstractor to
rewrite the attractive sentence. Moreover, since the sentence selection of the
extractor is not differentiable, techniques of reinforcement learning (RL) are
utilized to bridge the gap with rewards obtained from a popularity score
predictor. Through quantitative and qualitative experiments, we show that the
proposed PORL-HG significantly outperforms the state-of-the-art headline
generation models in terms of attractiveness evaluated by both human (71.03%)
and the predictor (at least 27.60%), while the faithfulness of PORL-HG is also
comparable to the state-of-the-art generation model.
