On Evaluating the Impact of Automatic Text Summarization on Large Document Classification: An Empirical Study

Document categorization continues to be a significant area of research, particularly in the context of automating the indexing of diverse web content such as blogs and forums. However, large document classification poses challenges in relation with both performance and processing time. This paper empirically investigates the impact of text summarization on document classification. For that, we compare classification performance before and after applying summarization techniques, focusing on two key aspects: (1) computational time and (2) classification performance, measured through accuracy, loss, and F1-score metrics. Our findings demonstrate the effectiveness of the proposed method, highlighting its potential to enhance the efficiency and accuracy of document categorization processes.