Data driven knowledge summarization of friction stir welded magnesium alloys literature by using natural language processing algorithms

• Published in: International journal on interactive design and manufacturing Vol. 18; no. 3; pp. 1113 - 1119
• Main Author: Mishra, Akshansh
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Aluminum; Artificial intelligence; CAE) and Design; Classifiers; Computer-Aided Engineering (CAD; Computers; Data collection; Data mining; Electronics and Microelectronics; Engineering; Engineering Design; Friction stir welding; Industrial Design; Instrumentation; Iranian literature; Knowledge; Language; Machine learning; Magnesium alloys; Magnesium base alloys; Mechanical Engineering; Natural language processing; Original Paper; Science; Speech recognition; Friction stir welding; Magnesium alloys; Artificial Intelligence; Natural Language Processing; Knowledge summarization
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-022-01118-2

Natural Language Processing is crucial because it clears up linguistic ambiguity and gives the data valuable quantitative structure for numerous downstream applications, including text analytics or speech recognition. In Natural Language Processing (NLP), knowledge summarization is the act of condensing information from lengthy texts for easier reading. In the present study, data were collected from the abstracts of the papers based on Friction Stir Welding Magnesium Alloys. These collected data were subjected to five Natural Language Processing based algorithms i.e., Text Rank, Lex Rank, Latent Semantic Analysis (LSA), Luhn (modulus 10), and KL-SUM for summarization purposes. The performance of these algorithms was evaluated by the ROUGE algorithm. The results demonstrated that the Luhn algorithm performs knowledge summarization with the greatest accuracy and F1 Score. By calculating the harmonic mean of a classifier’s precision and recall, the F1-score integrates both into a single metric. It is mainly used to evaluate the efficiency of two classifiers.