  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.
