Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms

TL;DR

This paper explores the use of NLP algorithms to extract key scientific information from research abstracts on Friction Stir Welding of Aluminum alloys, comparing their effectiveness using ROUGE scores.

Contribution

It applies four NLP algorithms to scientific abstracts in a novel context and evaluates their accuracy in extracting relevant information.

Findings

Luhn Algorithm achieved the highest F1-score of 0.413.

NLP algorithms can effectively summarize scientific research abstracts.

The study demonstrates the potential of NLP for automated scientific information extraction.

Abstract

Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise summary of a large amount of information, it becomes vital. If done manually, summarizing text can be costly and time-consuming. Natural Language Processing (NLP) is the sub-division of Artificial Intelligence that narrows down the gap between technology and human cognition by extracting the relevant information from the pile of data. In the present work, scientific information regarding the Friction Stir Welding of Aluminum alloys was collected from the abstract of scholarly research papers. For extracting the relevant information from these research abstracts four Natural Language Processing based algorithms i.e. Latent Semantic Analysis (LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn Algorithm resulted in the highest f1-Score of 0.413 in comparison to other algorithms.