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Large Language Model (LLM)-based text summarization has significantly advanced the generation of coherent and semantically rich summaries. However, these models still face critical limitations in real-world applications, particularly in multi-domain scenarios. Moreover, lack of explainability hinders the trust and reliability of generated summaries. To address these issues, we propose a novel eXplainable Multi-domAin document Summarization (X-MAS) framework that enhances both the performance and explainability of multi-domain text summarization. X-MAS utilizes semantic clustering of documents using BERT embeddings and HDBSCAN to discover the domains of each document in the corpus. Based on the domains, X-MAS utilizes domain-specific fine-tuned LLMs to generate the summary. Finally, X-MAS utilizes keyword matching and BERT to map summary content back to its source documents. We evaluate X-MAS on a real-world multi-domain document dataset and demonstrate that it outperforms existing methods in both summary quality and explainability.

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An Explainable Multi-domain Document Summarization Framework Using Domain-Aware Fine-Tuned Large Language Models

  • 会议论文

  • 发表日期：

    2026年

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  • Donggyu Kim,
  • Kunyoung Kim,
  • Mye Sohn

摘要

Large Language Model (LLM)-based text summarization has significantly advanced the generation of coherent and semantically rich summaries. However, these models still face critical limitations in real-world applications, particularly in multi-domain scenarios. Moreover, lack of explainability hinders the trust and reliability of generated summaries. To address these issues, we propose a novel eXplainable Multi-domAin document Summarization (X-MAS) framework that enhances both the performance and explainability of multi-domain text summarization. X-MAS utilizes semantic clustering of documents using BERT embeddings and HDBSCAN to discover the domains of each document in the corpus. Based on the domains, X-MAS utilizes domain-specific fine-tuned LLMs to generate the summary. Finally, X-MAS utilizes keyword matching and BERT to map summary content back to its source documents. We evaluate X-MAS on a real-world multi-domain document dataset and demonstrate that it outperforms existing methods in both summary quality and explainability.