This paper explores an automatic news generation and fact-checking system
based on language processing, aimed at enhancing the efficiency and quality of
news production while ensuring the authenticity and reliability of the news
content. With the rapid development of Natural Language Processing (NLP) and
deep learning technologies, automatic news generation systems are capable of
extracting key information from massive data and generating well-structured,
fluent news articles. Meanwhile, by integrating fact-checking technology, the
system can effectively prevent the spread of false news and improve the
accuracy and credibility of news. This study details the key technologies
involved in automatic news generation and factchecking, including text
generation, information extraction, and the application of knowledge graphs,
and validates the effectiveness of these technologies through experiments.
Additionally, the paper discusses the future development directions of
automatic news generation and fact-checking systems, emphasizing the importance
of further integration and innovation of technologies. The results show that
with continuous technological optimization and practical application, these
systems will play an increasingly important role in the future news industry,
providing more efficient and reliable news services.

本文探索了一种基于语言处理的自动新闻生成和事实核查系统，旨在提高新闻生产的效率和质量，同时确保新闻内容的真实性和可靠性。通过整合事实核查技术，该系统可以有效防止虚假新闻的传播，提高新闻的准确性和可信度，而自动新闻生成和事实核查所涉及的关键技术包括文本生成、信息提取和知识图谱的应用，并通过实验证实了这些技术的有效性。此外，本文讨论了自动新闻生成和事实核查系统的未来发展方向，强调了技术进一步整合和创新的重要性。研究结果表明，随着技术的不断优化和实际应用，这些系统在未来新闻行业中将发挥越来越重要的作用，提供更高效和可靠的新闻服务。