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%.

本文提出了一种基于句子级别的 beam search 方法的生成方法（SentBS），其中在生成过程中进行评估以选择适合后续生成的句子，旨在提高结构受控文本生成的效果，实验证明该方法可显著减少现有模型所遭受的约 68％的结构差异，并在结构控制的 MReD 数据集上取得了良好的效果。