  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.
