Optimizing Text Summarization With Hybrid AI Frameworks

Abstract

In natural language processing (NLP), text summarization is a crucial activity that aims to preserve the main ideas of the original material while collecting relevant information from vast textual data. Extractive and abstractive approaches are combined in hybrid text summarizing methods to provide more effective and cohesive summaries. This paper presents a proposed hybrid framework, describes the construction of an experiment to assess the hybrid approach's performance using Python, and offers a thorough literature overview of several hybrid text summarization techniques. To prove its effectiveness, the suggested framework is assessed using a few measures, and the outcomes are contrasted with those of current techniques.