Leveraging Non-dialogue Summaries for Dialogue Summarization
This work addresses a data scarcity problem for researchers and practitioners in dialogue summarization, though it is incremental as it adapts existing methods to a new context.
The paper tackles the lack of diverse dialogue summarization datasets by using non-dialogue summarization data to enhance dialogue summarization systems, resulting in significant performance improvements in zero- and few-shot settings and enhanced faithfulness across all training regimes.
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.