Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms
This work addresses the need for efficient information retrieval in a specific engineering domain, but it is incremental as it applies existing NLP methods to new data without introducing novel techniques.
The study tackled the problem of extracting relevant information from research abstracts on Friction Stir Welding of Aluminum alloys by applying four NLP-based summarization algorithms, with the Luhn Algorithm achieving the highest f1-Score of 0.413.
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.