Headline generation is a task of generating an appropriate headline for a
given article, which can be further used for machine-aided writing or enhancing
the click-through ratio. Current works only use the article itself in the
generation, but have not taken the writing style of headlines into
consideration. In this paper, we propose a novel Seq2Seq model called CLH3G
(Contrastive Learning enhanced Historical Headlines based Headline Generation)
which can use the historical headlines of the articles that the author wrote in
the past to improve the headline generation of current articles. By taking
historical headlines into account, we can integrate the stylistic features of
the author into our model, and generate a headline not only appropriate for the
article, but also consistent with the author's style. In order to efficiently
learn the stylistic features of the author, we further introduce a contrastive
learning based auxiliary task for the encoder of our model. Besides, we propose
two methods to use the learned stylistic features to guide both the pointer and
the decoder during the generation. Experimental results show that historical
headlines of the same user can improve the headline generation significantly,
and both the contrastive learning module and the two style features fusion
methods can further boost the performance.Comment: Accepted at EMNLP 202