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A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs

Proposes a training-free framework for multi-document summarization leveraging large language models and knowledge graphs.

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A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs

By Cuong Vuong Tuan, Trang Mai Xuan, Tien-Cuong Nguyen, Vu-Duc Ngo, Thien Van LuongarXiv
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The paper presents a training-free mixture-of-agents framework for multi-document summarization, decomposing the task into specialized agent tasks.

The approach leverages large language models and knowledge graphs to capture complex inter-document relationships without requiring labeled data or fine-tuning. Experiments demonstrate state-of-the-art or competitive performance across four datasets in English and Vietnamese.

Abstract

The paper presents a training-free mixture-of-agents framework for multi-document summarization, decomposing the task into specialized agent tasks. The approach leverages large language models and knowledge graphs to capture complex inter-document relationships without requiring labeled data or fine-tuning. Experiments demonstrate state-of-the-art or competitive performance across four datasets in English and Vietnamese.

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multi-document summarizationtraining-free frameworkllmsknowledge graphsagent-based approachKnowledge GraphsLarge Language ModelsAI AgentsContent Engineering
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A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs | Aramai