Performance Prediction of Data-Driven Knowledge summarization of High
  Entropy Alloys (HEAs) literature implementing Natural Language Processing
  algorithms
Akshansh Mishra Vijaykumar S Jatti Vaishnavi More Anish Dasgupta Devarrishi Dixit Eyob Messele Sefene
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.