  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%.
