Unleashing the potential of Natural Language Processing for News Link Article Summarization: Comparing TF-IDF and Text Rank Algorithms

Summarizing news links articles has become a crucial technique in the digital age because so much information is produced daily. It can be difficult for readers to stay updated with current events due to the abundance of news sources and the rapid pace of news production. By offering succinct and pertinent summaries, summarization helps control this information overload by facilitating the reader's access to information without requiring a large time commitment. A comparative study of two algorithms TF-IDF and Text Rank has been done in this work. These algorithms are implemented to generate the summary in a concise and precise manner and are evaluated using Rouge Scores such as Rouge 1, Rouge 2, and Rouge L with recall, precision, and f-score. The Experimental results show that compared with TFIDF, the average recall scores of the Text Rank algorithm are improved by 0.6% of Rouge 1, 0.15% of Rouge 2, and 1.9% of Rouge L respectively.