A wide range of control perspectives have been explored in controllable text generation. Structure-controlled summarization is recently proposed as a useful and interesting research direction. However, current structure-controlling methods have limited effectiveness in enforcing the desired structure. To address this limitation, we propose a sentence-level beam search generation method (SentBS), where evaluation is conducted throughout the generation process to select suitable sentences for subsequent generations. We experiment with different combinations of decoding methods to be used as subcomponents by SentBS and evaluate results on the structure-controlled dataset MReD. Experiments show that all explored combinations for SentBS can improve the agreement between the generated text and the desired structure, with the best method significantly reducing the structural discrepancies suffered by the existing model, by approximately 68%.

翻译：在可控文本生成过程中,探索了广泛的控制观点。最近有人提议结构控制的汇总是一个有用和有趣的研究方向。然而,目前的结构控制方法在执行理想结构方面效果有限。为解决这一局限性,我们提议采用句级波束搜索生成方法(SentBS),在整个生成过程中进行评估,为后代选择合适的句子。我们尝试将不同的解码方法组合作为SentBS的子构件,并评价结构控制数据数据集MReD的结果。 实验显示,SentBS的所有探索组合都能够改善生成文本与理想结构之间的一致,最佳方法将现有模型所遭受的结构性差异大幅降低约68%。</s>