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Related Experiment Video

Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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

Weihao Li¹, Dan Jiang¹, Han Zhang¹

¹School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China.

Peerj. Computer Science

|June 26, 2025

Summary

This summary is machine-generated.

This study introduces adaptive augmentation fusion (AAF) to improve dialogue summarization model training with limited data. AAF enhances model performance and generalization, outperforming other methods on benchmark datasets.

Keywords:

Abstract dialogue summarization Adaptive fusion augmentation Data augmentation

Related Experiment Videos

Last Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Natural Language Processing
Artificial Intelligence

Background:

Dialogue summarization is crucial for information retrieval.
Training abstract dialogue summarization models requires extensive labeled data.
Manual summarization is costly and time-consuming, hindering model development.

Purpose of the Study:

To address the challenge of insufficient annotated data for dialogue summarization.
To propose a novel data augmentation method, Adaptive Augmentation Fusion (AAF).
To balance model learning effectiveness and generalization capabilities in resource-constrained settings.

Main Methods:

Integrated Minor Perturbation Augmentation (MPA) and Semantic Reconstructive Augmentation (SRA) into AAF.
Developed a data augmentation strategy for dialogue summarization.
Evaluated the AAF method on DialogSum and SAMSum datasets.

Main Results:

AAF significantly improved ROUGE scores under resource-constrained conditions.
The proposed method outperformed baseline approaches.
The amount of augmented data critically impacts model training outcomes.

Conclusions:

AAF is an effective data augmentation technique for dialogue summarization.
The method offers a viable solution for training models with limited annotated data.
Code is publicly available for reproducibility and further research.