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

Performance Prediction of Data-Driven Knowledge summarization of High  Entropy Alloys (HEAs) literature implementing Natural Language Processing  algorithms