Semantic-Summarizer: Semantics-based text summarizer for English language text

Abstract

Text summarization is a process that condenses text documents for efficient information consumption. It offers numerous benefits, including time savings, reduced information overload, informed decision-making, and even content generation. In this paper, we present SemanticSum, an advanced text document summarizer that not only achieves compression but also preserves the underlying semantics of the original text. By leveraging semantic analysis, our summarizer intelligently identifies and removes redundant sentences that express the same meaning in different forms. Our experimental evaluation demonstrates that SemanticSum outperforms several state-of-the-art open-source summarizers regarding summary quality. The results validate the effectiveness of our approach, which capitalizes on semantics to produce more accurate and contextually meaningful summaries.