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

TL;DR

This study evaluates five NLP algorithms for summarizing High Entropy Alloys literature, finding that the Luhn algorithm performs best based on BLEU and ROUGE scores.

Contribution

It introduces the application of five NLP algorithms to HEAs literature summarization and compares their performance using standard metrics.

Findings

Luhn algorithm achieved the highest accuracy score.

BLEU and ROUGE scores were used for performance evaluation.

First application of these NLP algorithms to HEAs literature summarization.

Abstract

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.