Leveraging Non-dialogue Summaries for Dialogue Summarization

TL;DR

This paper proposes a method to improve dialogue summarization by leveraging non-dialogue summarization datasets through data transformation techniques, demonstrating significant benefits especially in low-resource scenarios.

Contribution

It introduces data transformation methods to adapt non-dialogue datasets for dialogue summarization, enhancing performance and faithfulness in low-resource settings.

Findings

Significant performance gains in zero- and few-shot settings.

Improved faithfulness to source text across training regimes.

Effective use of non-dialogue data for dialogue summarization.

Abstract

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.