A Comparative Study of Extractive Summary Algorithms Using Natural Language Processing

Language, an important feature in our daily life. It is a tool in communication used by bunions. Currently, there is an increasing number of articles and journals flooded on the Internet It is hard to read and study all the related articles to users' research areas manually because there is limited time for each people. One of the solutions is to summarize texts in the article. Natural Language Processing (NLP) is one of the features in :Machine Learning (ML) and it is used for summarization. This study was tried to investigate the performances of the three different extractive algorithms from ALP. The results were evaluated with the ROUGE evaluation package using ROUGE-1. ROUGE-2, ROUGE-L, and ROUGE-SU4 methods. 30 samples from the BBC dataset were used as the training data in the evaluation process. Results generated front ROUGE toolkit show the performance of the Barrios et at 's works is the best among the other two.