Conference

Machine learning approach for automatic document summarization

This paper investigates a machine learning-based approach for automatic document summarization, focusing on feature selection and learning patterns to determine key information for summaries. Traditional methods rely on various sentence selection features, each with strengths and limitations. Instead of an ad hoc combination, a trainable machine learning model is proposed to adapt across different text genres. The paper presents an architecture for document summarization, using machine learning techniques for efficient and adaptable summary generation.