# An adaptive fusion-based data augmentation method for abstract dialogue summarization

## TL;DR

This paper introduces a new data augmentation method for dialogue summarization that improves model performance with less labeled data.

## Contribution

The novel adaptive fusion method (AAF) combines two augmentation techniques to enhance model training under resource constraints.

## Key findings

- The AAF method improves ROUGE scores on DialogSum and SAMSum datasets compared to baselines.
- The amount of augmented data significantly affects model performance in resource-limited settings.

## Abstract

The dialogue summarization is necessary for information retrieval, and the training of abstract dialogue summarization models heavily rely on large amounts of labeled data. However, manual summarization of long dialogue is labor-costing and time-consuming. To solve this problem, this article proposes a data augmentation method for dialogue summary based on adaptive augmentation fusion (AAF), integrating the strengths of both Minor Perturbation Augmentation (MPA) and Semantic Reconstructive Augmentation (SRA) to balance model learning effectiveness and generalization capabilities. We first integrated existing enhancement methods to address the problem of insufficient annotated data for dialogue summarization. The experimental results on both the DialogSum and SAMSum datasets demonstrate that the AAF method achieves significant improvements in ROUGE scores under resource-constrained conditions, outperforming baseline approaches. Furthermore, it was validated that the selection of the amount of augmented data has a significant impact on model training results under resource-constrained conditions. We have publically released our code at https://github.com/alolke/AAF.