Automatic News Summerization
Kavach Dheer Arpit Dhankhar
Natural Language Processing is booming with its applications in the real
world, one of which is Text Summarization for large texts including news
articles. This research paper provides an extensive comparative evaluation of
extractive and abstractive approaches for news text summarization, with an
emphasis on the ROUGE score analysis. The study employs the CNN-Daily Mail
dataset, which consists of news articles and human-generated reference
summaries. The evaluation employs ROUGE scores to assess the efficacy and
quality of generated summaries. After Evaluation, we integrate the
best-performing models on a web application to assess their real-world
capabilities and user experience.