Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.

中文翻译：

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具有深度强化学习方法的多模式新闻提要评估系统

多语言和多模态数据分析是新兴的新闻提要评估系统。新闻提要分析和评估是相互关联的过程，有助于理解新闻因素。新闻提要评估系统可以针对单语言或多语言模型实现。用于多语言新闻分析的分类技术需要深层学习技术，而不是传统方法。在这项提议的工作中，实现了深度学习算法的层次结构，以制作有效的复杂新闻评估系统。开发了深度合作多语言强化学习模型、多维遗传算法和多语言生成对抗网络等深度学习技术来评估大量新闻提要。提议的技术以管道顺序协作，以构建深度新闻提要评估系统。实施细节表明，新提出的系统比其他新闻评估系统的性能要好 5% 到 12%。