  Text style transfer (TST) aims to vary the style polarity of text while
preserving the semantic content. Although recent advancements have demonstrated
remarkable progress in short TST, it remains a relatively straightforward task
with limited practical applications. The more comprehensive long TST task
presents two challenges: (1) existing methods encounter difficulties in
accurately evaluating content attributes in multiple words, leading to content
degradation; (2) the conventional vanilla style classifier loss encounters
obstacles in maintaining consistent style across multiple generated sentences.
  In this paper, we propose a novel method SC2, where a multilayer Joint
Style-Content Weighed (JSCW) module and a Style Consistency loss are designed
to address the two issues. The JSCW simultaneously assesses the amounts of
style and content attributes within a token, aiming to acquire a lossless
content representation and thereby enhancing content preservation. The multiple
JSCW layers further progressively refine content representations. We design a
style consistency loss to ensure the generated multiple sentences consistently
reflect the target style polarity. Moreover, we incorporate a denoising
non-autoregressive decoder to accelerate the training. We conduct plentiful
experiments and the results show significant improvements of SC2 over
competitive baselines. Our code: https://github.com/jiezhao6/SC2.
