SentBS: Sentence-level Beam Search for Controllable Summarization
This work addresses a specific challenge in controllable text generation for summarization, offering an incremental improvement over existing methods.
The paper tackled the problem of limited effectiveness in enforcing desired structures for structure-controlled summarization by proposing SentBS, a sentence-level beam search method that improved structural agreement, with the best method reducing structural discrepancies by approximately 68%.
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%.