用大模型自动总结新闻，帮企业快速识别供应链风险。
Evaluating the Effectiveness of Large Language Models in Automated News Article Summarization
- 用大模型聚合多源新闻并生成摘要，提升信息处理效率。
- 少样本微调的GPT-4o mini在风险识别上表现最佳。
- 适合需要实时监控供应链风险的企业和研究者。
自动化新闻分析与摘要有助于应对信息社会中海量信息的挑战。大语言模型（LLMs）能够将大量文本转化为简洁易懂的摘要，缓解信息过载问题，为用户提供关键信息概览。该技术在供应链风险分析中尤为重要：企业需监控供应商相关新闻，以满足合规要求、管理风险并保障供应链韧性。本文提出一种基于LLMs的自动化新闻摘要系统，整合多源新闻，通过大模型生成摘要，并以清晰格式呈现给用户，助力企业优化信息处理流程与决策。研究聚焦两个核心问题：(1) LLMs在自动化新闻摘要，尤其是供应链风险分析中是否有效？(2) 不同LLMs在可读性、重复检测和风险识别方面的摘要质量如何？我们采用当时公开可用的多种LLMs进行离线实验，并对表现最优的系统开展用户研究以进一步评估。结果表明，尤其在少样本设置下的GPT-4o mini显著提升了摘要质量与风险识别能力。
原文摘要 · Abstract (English)
The automation of news analysis and summarization presents a promising solution to the challenge of processing and analyzing vast amounts of information prevalent in today's information society. Large Language Models (LLMs) have demonstrated the capability to transform vast amounts of textual data into concise and easily comprehensible summaries, offering an effective solution to the problem of information overload and providing users with a quick overview of relevant information. A particularly significant application of this technology lies in supply chain risk analysis. Companies must monitor the news about their suppliers and respond to incidents for several critical reasons, including compliance with laws and regulations, risk management, and maintaining supply chain resilience. This paper develops an automated news summarization system for supply chain risk analysis using LLMs. The proposed solution aggregates news from various sources, summarizes them using LLMs, and presents the condensed information to users in a clear and concise format. This approach enables companies to optimize their information processing and make informed decisions. Our study addresses two main research questions: (1) Are LLMs effective in automating news summarization, particularly in the context of supply chain risk analysis? (2) How effective are various LLMs in terms of readability, duplicate detection, and risk identification in their summarization quality? In this paper, we conducted an offline study using a range of publicly available LLMs at the time and complemented it with a user study focused on the top performing systems of the offline experiments to evaluate their effectiveness further. Our results demonstrate that LLMs, particularly Few-Shot GPT-4o mini, offer significant improvements in summary quality and risk identification.
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