“The goal of headline style transfer in this paper is to make a headline more attractive whilemaintaining its meaning. The absence of parallel training data is one of the main problems in thisfield. In this work, we design a discrete style space for unsupervised headline style transfer, shortfor D-HST. This model decomposes the style-dependent text generation into content-featureextraction and style modelling. Then, generation decoder receives input from content, style,and their mixing components. In particular, it is considered that textual style signal is moreabstract than the text itself. Therefore, we propose to model the style representation space asa discrete space, and each discrete point corresponds to a particular category of the styles thatcan be elicited by syntactic structure. Finally, we provide a new style-transfer dataset, namedas TechST, which focuses on transferring news headline into those that are more eye-catchingin technical social media. In the experiments, we develop two automatic evaluation metrics— style transfer rate (STR) and style-content trade-off (SCT) — along with a few traditionalcriteria to assess the overall effectiveness of the style transfer. In addition, the human evaluationis thoroughly conducted in terms of assessing the generation quality and creatively mimicking ascenario in which a user clicks on appealing headlines to determine the click-through rate. Ourresults indicate the D-HST achieves state-of-the-art results in these comprehensive evaluations. Introduction”