# Leveraging Non-dialogue Summaries for Dialogue Summarization

**Authors:** Seongmin Park, Dongchan Shin, Jihwa Lee  
**arXiv:** [2210.09474](https://arxiv.org/abs/2210.09474)  
**Date:** 2022-10-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.09474v1/

## Key points

- Authors Seongmin Park, Dongchan Shin, and Jihwa Lee propose a novel approach to dialogue summarization that leverages non-dialogue summarization data.
- The approach involves transforming document summarization data pairs to create training data suitable for dialogue summarization tasks while maintaining key characteristics of non-dialogue datasets.
- Extensive experiments in English and Korean languages validate the effectiveness of the proposed approach.
- Results indicate that incorporating non-dialogue data leads to significant improvements in summarization performance across zero- and few-shot settings with enhanced faithfulness to the original text.
- The study highlights the potential benefits of integrating diverse sources of data for enhancing dialogue summarization systems and improving performance and fidelity in automated text summarization tasks.

## Summary

In their paper titled "Leveraging Non-dialogue Summaries for Dialogue Summarization," authors Seongmin Park, Dongchan Shin, and Jihwa Lee address the challenge of limited diverse dialogue summarization datasets in academia. They propose a novel approach that utilizes non-dialogue summarization data to enhance dialogue summarization systems. This involves applying transformations to document summarization data pairs to create training data better suited for dialogue summarization tasks while maintaining key characteristics of non-dialogue datasets such as improved faithfulness to the source text. The authors conducted extensive experiments in both English and Korean languages to validate the effectiveness of their approach. Results show that incorporating non-dialogue data leads to significant improvements in summarization performance across zero- and few-shot settings with enhanced faithfulness to the original text. Overall, this study highlights the potential benefits of leveraging non-dialogue summaries for enhancing dialogue summarization systems and suggests that integrating diverse sources of data can lead to improved performance and fidelity in automated text summarization tasks, ultimately contributing to advancements in natural language processing research.