SCS-VAE: Generate Style Headlines Via Novel Disentanglement
Zhu Zhaoqian, Xiang Lin, Gongshen Liu, Bo Su, Tianhe Lu · 2023
Current headline generation models only focus on consistent headlines while lacking attention to headline styles. However, headlines of different styles could have different effects on producers and viewers. Capturing salient content while following a unique style is challenging when generating text. In this paper, we propose a Semantic Content-Style VAE(SCS-VAE), combining the Variational Auto-Encoder(VAE) and the dictionary learning method to solve the content-related and stylized problems of generating headlines simultaneously. Specifically, we disentangle the semantic information in content and style space, allowing us to control the generation of headline content and styles. Moreover, we design interpretable loss functions better to supervise the semantic information into two different feature spaces. Experiment results show that SCS-VAE has SOTA performance in headline generation method and makes the style of the headlines more diversified.