The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.

翻译：各领域非结构化数据的快速增长使得多文档理解与摘要生成成为关键任务。传统方法往往难以捕获相关上下文、保持逻辑一致性并从冗长文档中提取关键信息。本文探讨利用长上下文大语言模型进行多文档摘要生成，论证其具备把握广泛关联、提供连贯摘要、适应不同行业领域及与企业应用/系统集成的卓越能力。本文系统阐述了为有效部署长上下文大语言模型而设计的多文档摘要工作流程，并通过法律应用、人力资源/财务/采购等企业职能以及医疗与新闻领域的案例研究予以佐证。这些案例研究显示其在效率与准确性方面均取得显著提升。本文深入剖析了数据集多样性、模型可扩展性等技术障碍，以及偏见消除与事实准确性等伦理考量。最后提出增强长上下文大语言模型功能与应用前景的研究路径，确立其作为跨领域信息处理与企业应用转型的关键工具地位。