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：摘要：在可控文本生成领域，多种控制视角已得到广泛探索。结构可控摘要作为一项实用且有趣的研究方向近期被提出，然而当前结构控制方法在强化目标结构效果方面存在局限。针对这一局限，我们提出了一种句子级束搜索生成方法（SentBS），该方法在生成过程中持续进行评估，以筛选适合后续生成的句子。我们通过不同解码方法的组合实验作为SentBS的子组件，并在结构可控数据集MReD上评估结果。实验表明，SentBS的所有组合方案均能提升生成文本与目标结构的一致性，其中最优方法将现有模型存在的结构偏差降低约68%。