News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.

翻译：新闻摘要生成是情报分析领域的一项重要任务，能够提供准确全面的信息，帮助人们更好地理解和应对复杂的现实世界事件。然而，传统新闻摘要生成方法面临一些挑战：受限于模型自身和训练数据量，同时受到文本噪声的影响，难以准确生成可靠信息。本文提出了一种利用大语言模型（LLM）的强大自然语言理解与生成能力进行新闻摘要生成的新范式。我们使用LLM从新闻段落包含的事件中提取多个结构化事件模式，通过遗传算法对事件模式种群进行进化，并选择最适应的事件模式输入LLM以生成新闻摘要。我们设计了一个新闻摘要生成器（NSG），用于选择和进化事件模式种群并生成新闻摘要。实验结果表明，该新闻摘要生成器能够生成准确可靠的新闻摘要，并具有一定的泛化能力。