Contrastive Learning enhanced Author-Style Headline Generation

• URL: http://arxiv.org/abs/2211.03305v1
• Date: Mon, 7 Nov 2022 04:51:03 GMT
• Title: Contrastive Learning enhanced Author-Style Headline Generation
• Authors: Hui Liu, Weidong Guo, Yige Chen and Xiangyang Li
• Abstract summary: We propose a novel Seq2Seq model called CLH3G (Contrastive Learning enhanced Historical Headlines based Headline Generation) By taking historical headlines into account, we can integrate the stylistic features of the author into our model, and generate a headline consistent with the author's style. Experimental results show that historical headlines of the same user can improve the headline generation significantly.
• Score: 15.391087541824279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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