Dialogue Summarization with Mixture of Experts based on Large Language Models
Open accessAbstract
Dialogue summarization is an important task that requires to generate highlights for a conversation from different aspects (e.g., content of various speakers).While several studies successfully employ large language models (LLMs) and achieve satisfying results, they are limited by using one model at a time or treat it as a black box, which makes it hard to discriminatively learn essential content in a dialogue from different aspects, therefore may lead to anticipation bias and potential loss of information in the produced summaries.In this paper, we propose an LLM-based approach with roleoriented routing and fusion generation to utilize mixture of experts (MoE) for dialogue summarization.Specifically, the role-oriented routing is an LLM-based module that selects appropriate experts to process different information; fusion generation is another LLM-based module to locate salient information and produce finalized dialogue summaries.The proposed approach offers an alternative solution to employing multiple LLMs for dialogue summarization by leveraging their capabilities of in-context processing and generation in an effective manner.We run experiments on widely used benchmark datasets for this task, where the results demonstrate the superiority of our approach in producing informative and accurate dialogue summarization. 1
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Multimodal models integrate sensory inputs such as text, vision, and audio, and are crucial for comprehensive understanding of complex data sets, particularly transformative in medical imaging.
“Multimodal models, which integrate a variety of sensory inputs such as text, vision, and audio, are crucial in achieving a comprehensive understanding of complex data sets, particularly transformative in fields like medical imaging [113], [112], [115].”
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From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [112]
Recent developments like Gemini model have set new benchmarks by demonstrating state-of-the-art performance in various multimodal tasks, including natural image, audio, and video understanding, and mathematical reasoning.
“Recent developments like Gemini model have set new benchmarks by demonstrating state-of-the-art performance in various multimodal tasks, including natural image, audio, and video understanding, and mathematical reasoning [112].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [112]
Gemini's inherently multimodal design exemplifies seamless integration and operation across different information types.
“Gemini's inherently multimodal design exemplifies the seamless integration and operation across different information types [112].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [112]
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A Review on Edge Large Language Models: Design, Execution, and Applications
Zheng, Y., Chen, Y., Qian, B., Shi, X., Shu, Y., & Chen, J. (2025) · ACM Computing Surveys · doi:10.1145/3719664 · Cited by 169 · see citing works
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
- Authors
- Tian, Y., Xia, F., & Song, Y.
- Affiliations
- University of Science and Technology of China (CN)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 7143-7155
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402672051
- Cited by
- 14
- References
- 13
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 2
- FWCI
- 4.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Key Research and Development Program of China (2021YFF0901600)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- dialogue summarization, mixture of experts, large language models, multi-speaker conversations
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