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    <abstract>With the advent of large language models, the complexity of multi-document summarization task has been substantially reduced. The summarization process must effectively handle noisy documents that are irrelevant to the main topic while preserving essential information. Recently, Chain-of-Density (COD) and Chain-of-Event (CoE) have proposed prompts to effectively handle the noisy documents by using entity-centric approaches for the summarization. However, CoD and CoE are prone to information loss during entity extraction due to their tendency to overly filter out entities perceived as less critical but that could still be important. In this paper, we propose a novel instruction prompt termed as Chain of Knowledge Graph (CoKG) for multi-document summarization. Our prompt extracts entities and constructs relationships between entities to form a Knowledge Graph (KG). Next, the prompt enriches these relationships to recognize potentially important entities and assess the strength of each relation. If the acquired KG meets a predefined quality level, the KG is used to summarize the given documents. This process helps alleviate the information loss in multi-document summarization. Experimental results demonstrate that our prompt effectively preserves key entities and is robust to noisy documents.</abstract>
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%0 Conference Proceedings
%T Chain of Knowledge Graph: Information-Preserving Multi-Document Summarization for Noisy Documents
%A Lee, Kangil
%A Jang, Jinwoo
%A Lim, Youngjin
%A Shin, Minsu
%Y Liu, Kang
%Y Song, Yangqiu
%Y Han, Zhen
%Y Sifa, Rafet
%Y He, Shizhu
%Y Long, Yunfei
%S Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025
%D 2025
%8 January
%I ELRA and ICCL
%C Abu Dhabi, UAE
%F lee-etal-2025-chain
%X With the advent of large language models, the complexity of multi-document summarization task has been substantially reduced. The summarization process must effectively handle noisy documents that are irrelevant to the main topic while preserving essential information. Recently, Chain-of-Density (COD) and Chain-of-Event (CoE) have proposed prompts to effectively handle the noisy documents by using entity-centric approaches for the summarization. However, CoD and CoE are prone to information loss during entity extraction due to their tendency to overly filter out entities perceived as less critical but that could still be important. In this paper, we propose a novel instruction prompt termed as Chain of Knowledge Graph (CoKG) for multi-document summarization. Our prompt extracts entities and constructs relationships between entities to form a Knowledge Graph (KG). Next, the prompt enriches these relationships to recognize potentially important entities and assess the strength of each relation. If the acquired KG meets a predefined quality level, the KG is used to summarize the given documents. This process helps alleviate the information loss in multi-document summarization. Experimental results demonstrate that our prompt effectively preserves key entities and is robust to noisy documents.
%U https://aclanthology.org/2025.neusymbridge-1.1/
%P 1-5

Markdown (Informal)

[Chain of Knowledge Graph: Information-Preserving Multi-Document Summarization for Noisy Documents](https://aclanthology.org/2025.neusymbridge-1.1/) (Lee et al., NeusymBridge 2025)

• Chain of Knowledge Graph: Information-Preserving Multi-Document Summarization for Noisy Documents (Lee et al., NeusymBridge 2025)

ACL

• Kangil Lee, Jinwoo Jang, Youngjin Lim, and Minsu Shin. 2025. Chain of Knowledge Graph: Information-Preserving Multi-Document Summarization for Noisy Documents. In Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025, pages 1–5, Abu Dhabi, UAE. ELRA and ICCL.