Unified Argument Retrieval System from German News Articles Using Large Language Models

The rapid growth in the number of news articles published daily can create challenges for users to explore specific topics and gather different perspectives around the topics to make neutral and unbiased conclusions. The system's ability to intelligently cluster news articles from multiple sources and retrieve concise (pro/con) relevant arguments is necessary for users' well-informed decision-making. In this paper, we introduce our unified argument retrieval system that uses our clustering model to cluster news articles and subsequently extracts the core arguments from news articles using the argument prediction model. We conducted a user study to understand the system's usability and users' satisfaction with the quality of clusters and arguments extracted.