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Automatic News Generation and Fact-Checking System Based on Language Processing

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arxiv 2405.10492 v2 pith:KBLYYGTN submitted 2024-05-17 cs.CL cs.LG

Xirui Peng , Qiming Xu , Zheng Feng , Haopeng Zhao , Lianghao Tan , Yan Zhou , Zecheng Zhang , Chenwei Gong
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Yingqiao Zheng
This is my paper · ORCID
classification cs.CLcs.LG
keywords newsgenerationautomaticfact-checkingtechnologieslanguageprocessingsystem
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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