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

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ID: 313158
2023
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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.
Reference Key
imported_1777059468_69ebc68cb34e2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kamlish
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/197
Keywords Keywords not found

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