title: Multi-LLM Text Summarization
summary: 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.
paper_authors._id: 676984a1edea1efd81017959
paper_authors.name: Jiangnan Fang
paper_authors._id: 676984a1edea1efd8101795a
paper_authors.name: Cheng-Tse Liu
paper_authors._id: 676984a1edea1efd8101795b
paper_authors.name: Jieun Kim
paper_authors._id: 676984a1edea1efd8101795c
paper_authors.name: Yash Bhedaru
paper_authors._id: 676984a1edea1efd8101795d
paper_authors.name: Ethan Liu
paper_authors._id: 676984a1edea1efd8101795e
paper_authors.name: Nikhil Singh
paper_authors._id: 676984a1edea1efd8101795f
paper_authors.name: Nedim Lipka
paper_authors._id: 676984a1edea1efd81017960
paper_authors.name: Puneet Mathur
paper_authors._id: 676984a1edea1efd81017961
paper_authors.name: Nesreen K. Ahmed
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-07T08:42:50.165Z
paper_authors._id: 663e588ccca86c8371a913d9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/663e588ccca86c8371a913d9/wgVGw5U77cqwtjovhsXyR.jpeg
paper_authors.fullname: Nesreen Ahmed
paper_authors.type: user
paper_authors.user: nkahmed
paper_authors._id: 676984a1edea1efd81017962
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:36:09.059Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_authors._id: 676984a1edea1efd81017963
paper_authors.name: Ryan A. Rossi
paper_authors._id: 676984a1edea1efd81017964
paper_authors.name: Hanieh Deilamsalehy
paper_title: Multi-LLM Text Summarization
paper_summary: 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.
paper_ai_keywords: Multi-LLM summarization framework decentralized summarization centralized summarization LLMs (Large Language Models) diverse summaries single LLM evaluation multi-LLM evaluation multi-LLM approaches