Generic and Domain Centred Summarization of Documents with Heightened Thought Plan by using Deep Learning Techniques

In recent years, the massive growth of digital content has created a pressing need for effective summarization tools that can deliver concise, coherent, and informative representations of lengthy texts. This study offers a comprehensive model that uses deep learning to discern and emphasize the underlying thought patterns within documents, ensuring that the generated summaries not only capture key factual details but also the nuanced domain-centric insights. Document summarization stands at the intersection of information retrieval and natural language processing. The Heightened Thought Plan Summarizer (HTPS) introduces a cutting-edge approach, adept at generic and domain-centric summarization. By converting sentences into embeddings and calculating domain relevance scores, HTPS crafts summaries that are both broad and nuanced. Our evaluations, spanning performance, efficiency, and domain relevance, pitch HTPS against ten traditional methods. The results affirm HTPS's superiority in producing human-like, domain-relevant, and coherent summaries. While there's potential for refining its efficiency and coverage, HTPS represents a promising stride in advanced document summarization.