@inproceedings{fang-etal-2025-multi,
title = "Multi-{LLM} Text Summarization",
author = "Fang, Jiangnan and
Liu, Cheng-Tse and
Kim, Jieun and
Bhedaru, Yash and
Liu, Ethan and
Singh, Nikhil and
Lipka, Nedim and
Mathur, Puneet and
Ahmed, Nesreen K. and
Dernoncourt, Franck and
Rossi, Ryan and
Deilamsalehy, Hanieh",
editor = "Angelova, Galia and
Kunilovskaya, Maria and
Escribe, Marie and
Mitkov, Ruslan",
booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ranlp-1.43/",
pages = "352--362",
abstract = "In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depending on whether our multi-LLM decentralized summarization is used or centralized. In both our multi-LLM decentralized and centralized strategies, we have k different LLMs that generate diverse summaries of the text. However, during evaluation, our multi-LLM centralized summarization approach leverages a single LLM to evaluate the summaries and select the best one whereas k LLMs are used for decentralized multi-LLM summarization. Overall, we find that our multi-LLM summarization approaches significantly outperform the baselines that leverage only a single LLM by up to 3x. These results indicate the effectiveness of multi-LLM approaches for summarization."
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%0 Conference Proceedings
%T Multi-LLM Text Summarization
%A Fang, Jiangnan
%A Liu, Cheng-Tse
%A Kim, Jieun
%A Bhedaru, Yash
%A Liu, Ethan
%A Singh, Nikhil
%A Lipka, Nedim
%A Mathur, Puneet
%A Ahmed, Nesreen K.
%A Dernoncourt, Franck
%A Rossi, Ryan
%A Deilamsalehy, Hanieh
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F fang-etal-2025-multi
%X In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depending on whether our multi-LLM decentralized summarization is used or centralized. In both our multi-LLM decentralized and centralized strategies, we have k different LLMs that generate diverse summaries of the text. However, during evaluation, our multi-LLM centralized summarization approach leverages a single LLM to evaluate the summaries and select the best one whereas k LLMs are used for decentralized multi-LLM summarization. Overall, we find that our multi-LLM summarization approaches significantly outperform the baselines that leverage only a single LLM by up to 3x. These results indicate the effectiveness of multi-LLM approaches for summarization.
%U https://aclanthology.org/2025.ranlp-1.43/
%P 352-362
Markdown (Informal)
[Multi-LLM Text Summarization](https://aclanthology.org/2025.ranlp-1.43/) (Fang et al., RANLP 2025)
ACL
- Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, Yash Bhedaru, Ethan Liu, Nikhil Singh, Nedim Lipka, Puneet Mathur, Nesreen K. Ahmed, Franck Dernoncourt, Ryan Rossi, and Hanieh Deilamsalehy. 2025. Multi-LLM Text Summarization. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 352–362, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.