A trainable document summarizer using Bayesian classifier approach
This paper investigates a machine learning-based approach for document summarization, focusing on feature selection and learning patterns that determine the most relevant information for summaries. Instead of relying on ad hoc feature selection, the study leverages trainable machine learning techniques, ensuring adaptability across different text genres. The paper discusses the design, implementation, and performance of a Bayesian classifier for document summarization, highlighting its effectiveness in automating the summarization process.
