Enhancing Automated Text Summarization: A Survey and Novel Method with Semantic Information for Domain-Specific Summaries

Abstract

In the contemporary landscape of information overload, efficient text summarization techniques have emerged as indispensable tools for distilling crucial insights and managing the ever-expanding volume of textual data. This paper introduces a novel approach to domain-specific summarization that integrates the power of Semantic Analysis to amplify the summarization process. Amid the well-established paradigms of extractive and abstractive methods, this study emphasizes the evolving trends of abstractive summarization techniques, including real-time summarization capabilities. The historical roots of automated text summarization trace back to the early 1950s, and this field has witnessed substantial growth, especially with the availability of NLP tools and techniques in Python. This study underscores the practicality and efficiency of automated summarization systems, thereby alleviating the need for manual intervention in the summarization process. A distinguishing feature of this research is the incorporation of Semantic Analysis, a relatively underexplored avenue in the field of summarization. By leveraging Semantic Analysis, the proposed methodology improves keyword identification and elevates the quality of generated summaries. This novel approach bridges a gap in the understanding of semantic structures in text summarization, demonstrating the synergistic potential of linguistic analysis and technology.

At its core, the innovation lies in the ability to extract pertinent information while preserving the nuances of the source text. By harnessing the power of Semantic Analysis, the summarization model captures the essence of the text, resulting in concise summaries that retain essential content and contextual significance. The utilization of semantic knowledge promises improved summarization accuracy and quality. The impact of this research extends to various applications, including information retrieval, document clustering, and knowledge extraction. By enhancing summarization effectiveness and minimizing manual effort, the proposed approach contributes significantly to the field of text summarization. It underscores the critical role of linguistic understanding in automated processes and presents a valuable tool for navigating the challenges posed by the exponential growth of textual data in today's information-driven landscape.