Financial markets change rapidly due to news, economic shifts, and geopolitical events. Quick reactions are vital for investors to avoid losses or capture short-term gains. As a result, concise financial news summaries are critical for decision-making. With over 50,000 financial articles published daily, automation in summarization is necessary. This study evaluates a range of summarization methods, from simple extractive techniques to advanced large language models (LLMs), using the FinLLMs Challenge dataset. LLMs generated more coherent and informative summaries, but they are resource-intensive and prone to hallucinations, which can introduce significant errors into financial summaries. In contrast, extractive methods perform well on short, well-structured texts and offer a more efficient alternative for this type of article. The best ROUGE results come from fine-tuned LLM model like FT-Mistral-7B, although our data corpus has limited reliability, which calls for cautious interpretation.
翻译：金融市场因新闻、经济变动和地缘政治事件而快速变化。投资者需要迅速反应以避免损失或捕捉短期收益。因此，简洁的金融新闻摘要对于决策至关重要。鉴于每日发布的金融文章超过50,000篇，摘要自动化成为必要。本研究使用FinLLMs挑战数据集，评估了从简单抽取式技术到先进大型语言模型（LLMs）的一系列摘要方法。LLMs生成的摘要更具连贯性和信息量，但它们资源密集且易产生幻觉，这可能给金融摘要引入重大错误。相比之下，抽取式方法在简短、结构良好的文本上表现优异，为此类文章提供了更高效的替代方案。最佳ROUGE结果来自微调的LLM模型（如FT-Mistral-7B），尽管我们的数据语料库可靠性有限，这要求谨慎解读结果。
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