The ability to interpret spoken language is connected to natural language processing. It involves teaching the AI how words relate to one another, how they are meant to be used, and in what settings. The goal of natural language processing (NLP) is to get a machine intelligence to process words the same way a human brain does. This enables machine intelligence to interpret, arrange, and comprehend textual data by processing the natural language. The technology can comprehend what is communicated, whether it be through speech or writing because AI pro-cesses language more quickly than humans can. In the present study, five NLP algorithms, namely, Geneism, Sumy, Luhn, Latent Semantic Analysis (LSA), and Kull-back-Liebler (KL) al-gorithm, are implemented for the first time for the knowledge summarization purpose of the High Entropy Alloys (HEAs). The performance prediction of these algorithms is made by using the BLEU score and ROUGE score. The results showed that the Luhn algorithm has the highest accuracy score for the knowledge summarization tasks compared to the other used algorithms.

翻译：口语理解能力与自然语言处理密切相关，其核心在于训练人工智能理解词汇间的关联方式、使用意图及语境。自然语言处理的目标是让机器智能像人类大脑一样处理词汇，从而通过自然语言处理技术实现文本数据的解释、组织和理解。由于人工智能处理语言的速度超越人类，该技术可理解口语或书面语所传达的信息。本研究首次将五种自然语言处理算法——Geneism、Sumy、Luhn、潜在语义分析算法和KL算法——应用于高熵合金文献的知识摘要任务，并通过BLEU分数和ROUGE分数对这些算法的性能进行预测。结果表明，相较于其他算法，Luhn算法在知识摘要任务中展现出最高的准确率评分。