Improving Abstractive News Summarization Using Keyword Extraction for Human-Like Summaries

In today’s world, people frequently find themselves reluctant to read lengthy news articles, making news summarization a crucial tool for efficient information consumption. This study investigates the use of Large Language Models (LLMs), specifically BART and DistilBART, to generate abstractive news summaries based on the CNN Daily Mail News dataset, which includes text and highlight data. To address the limitation of highlights that often only capture parts of sentences, keyword extraction techniques such as RAKE, YAKE, and KeyBERT were incorporated to guide the summarization method, improving the quality of the generated summaries. The results demonstrate that the BART and DistilBART models, when combined with keyword extraction methods, produce more balanced and coherent summaries compared to the gold summaries. The BART model combined with RAKE obtained the greatest ROUGE-1 rating of 0.3733, while the DistilBART model with KeyBERT(paraphrase-MiniLM-L6-v2) reached a ROUGE-1 score of 0.3722, showcasing the effectiveness of keyword extraction techniques in improving news summarization performance but with just the right tuned hyperparameters.