Development of Automated News Content Generation System based on Deep Learning Framework

This study is dedicated to testing the effect of an automated news generation system that combines a bidirectional long short-term memory network with an attention mechanism. This study builds a system that can process complex text data and generate high-quality news content. The system uses advanced natural language processing techniques, mainly including Skip-Gram word embedding, Bi-LSTM, attention mechanism, and unidirectional decoder LSTM. This study is based on a standardized data set containing 5,000 news articles. The performance of the system is comprehensively evaluated. In the evaluation process, a variety of evaluation indicators such as BLEU, ROUGE, and METEOR are used. The experimental data show that the language similarity score of this system on a specific data set is 0.72, the text evaluation index ROUGE score is 0.68, and the semantic similarity METEOR score is 0.65, which reflects the excellent performance of the system in generating language and the high accuracy of information transmission. The conclusion of this study shows that the automated news content generation system ensures the relevance and accuracy of the content while improving the efficiency of news output, thus showing that it has broad application potential and development space.