Text Summarization on News Aggregation
Olivia Tirso
Co-Presenters: Individual Presentation
College: The Dorothy and George Hennings College of Science, Mathematics and Technology
Major: Computer Information Systems (M.S.)
Faculty Research Mentor: Daehan Kwak
Abstract:
The rapid and continuous growth of online news content, driven by the increasing number of articles published daily across various platforms, presents a significant challenge for readers. The overwhelming volume of information makes it more difficult for individuals to remain well-informed by spending excessive time filtering through content. This surge in information availability creates an urgent need for effective solutions to help manage the influx of data while ensuring that readers have access to the most relevant and concise news updates. The research outlined in this paper aims to address this challenge by developing and evaluating automated methods for text summarization and news classification, specifically focusing on optimizing news aggregation.