@inproceedings{lu-etal-2025-mutual,
title = "Mutual Reinforcement of {LLM} Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization",
author = "Lu, Yen-Ju and
Hu, Ting-Yao and
Koppula, Hema Swetha and
Pouransari, Hadi and
Chang, Jen-Hao Rick and
Xia, Yin and
Kong, Xiang and
Zhu, Qi and
Wang, Xiaoming Simon and
Tuzel, Oncel and
Vemulapalli, Raviteja",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.404/",
doi = "10.18653/v1/2025.findings-naacl.404",
pages = "7237--7256",
ISBN = "979-8-89176-195-7",
abstract = "In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge, we mutually reinforce the LLM{'}s dialogue synthesis and summarization capabilities, allowing them to complement each other during training and enhance overall performances. The dialogue synthesis capability is enhanced by directed preference optimization with preference scoring from summarization capability. The summarization capability is enhanced by the additional high quality dialogue-summary paired data produced by the dialogue synthesis capability. By leveraging the proposed MRDS mechanism, we elicit the internal knowledge of LLM in the format of synthetic data, and use it to augment the few-shot real training dataset. Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5{\%} increase in ROUGE scores and a 0.3{\%} improvement in BERT scores in few-shot settings. Furthermore, our method attains the highest average scores in human evaluations, surpassing both the pre-trained models and the baselines fine-tuned solely for summarization tasks."
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<abstract>In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge, we mutually reinforce the LLM’s dialogue synthesis and summarization capabilities, allowing them to complement each other during training and enhance overall performances. The dialogue synthesis capability is enhanced by directed preference optimization with preference scoring from summarization capability. The summarization capability is enhanced by the additional high quality dialogue-summary paired data produced by the dialogue synthesis capability. By leveraging the proposed MRDS mechanism, we elicit the internal knowledge of LLM in the format of synthetic data, and use it to augment the few-shot real training dataset. Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. Furthermore, our method attains the highest average scores in human evaluations, surpassing both the pre-trained models and the baselines fine-tuned solely for summarization tasks.</abstract>
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%0 Conference Proceedings
%T Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization
%A Lu, Yen-Ju
%A Hu, Ting-Yao
%A Koppula, Hema Swetha
%A Pouransari, Hadi
%A Chang, Jen-Hao Rick
%A Xia, Yin
%A Kong, Xiang
%A Zhu, Qi
%A Wang, Xiaoming Simon
%A Tuzel, Oncel
%A Vemulapalli, Raviteja
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F lu-etal-2025-mutual
%X In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge, we mutually reinforce the LLM’s dialogue synthesis and summarization capabilities, allowing them to complement each other during training and enhance overall performances. The dialogue synthesis capability is enhanced by directed preference optimization with preference scoring from summarization capability. The summarization capability is enhanced by the additional high quality dialogue-summary paired data produced by the dialogue synthesis capability. By leveraging the proposed MRDS mechanism, we elicit the internal knowledge of LLM in the format of synthetic data, and use it to augment the few-shot real training dataset. Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. Furthermore, our method attains the highest average scores in human evaluations, surpassing both the pre-trained models and the baselines fine-tuned solely for summarization tasks.
%R 10.18653/v1/2025.findings-naacl.404
%U https://aclanthology.org/2025.findings-naacl.404/
%U https://doi.org/10.18653/v1/2025.findings-naacl.404
%P 7237-7256
Markdown (Informal)
[Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization](https://aclanthology.org/2025.findings-naacl.404/) (Lu et al., Findings 2025)
ACL
- Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari, Jen-Hao Rick Chang, Yin Xia, Xiang Kong, Qi Zhu, Xiaoming Simon Wang, Oncel Tuzel, and Raviteja Vemulapalli. 2025. Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 7237–7256, Albuquerque, New Mexico. Association for Computational Linguistics.