  To mitigate the lack of diverse dialogue summarization datasets in academia,
we present methods to utilize non-dialogue summarization data for enhancing
dialogue summarization systems. We apply transformations to document
summarization data pairs to create training data that better befit dialogue
summarization. The suggested transformations also retain desirable properties
of non-dialogue datasets, such as improved faithfulness to the source text. We
conduct extensive experiments across both English and Korean to verify our
approach. Although absolute gains in ROUGE naturally plateau as more dialogue
summarization samples are introduced, utilizing non-dialogue data for training
significantly improves summarization performance in zero- and few-shot settings
and enhances faithfulness across all training regimes.

Computation and Language;Artificial Intelligence