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.

本文提出了一种名为 CLH3G 的新型 Seq2Seq 模型，该模型可以利用作者过去写过的文章的历史标题，以改善当前文章的标题生成。通过考虑历史标题，我们可以将作者的风格特征融入到我们的模型中，生成既适合文章又与作者的风格一致的标题。我们进一步引入了一种基于对比学习的编码器辅助任务来高效地学习作者的风格特征，以及两种使用学习的风格特征来指导指针和解码器的方法。实验结果表明，相同作者的历史标题可以显著改善标题生成，并且对比学习模块和两种风格特征融合方法可以进一步提高性能。

对比学习增强的作者风格标题生成