Abstract:
In an environment where the sheer volume of news articles makes it challenging for readers to
quickly access relevant information, traditional text summarization techniques often fail to
maintain contextual richness and factual accuracy. Ontology-based prompt tweaking and
abstractive summarizing techniques are combined in this study's hybrid approach to provide news
summaries that are accurate, succinct, and contextually relevant. By embedding domain- specific
knowledge through ontologies into the summarization process, the study addresses the
shortcomings of conventional summarization models and enhances the relevance and clarity of
the generated outputs.
Motivated by the increasing demand for efficient information retrieval, the research develops a
framework that automates summarization through structured knowledge representation and
iterative prompt refinement. Using a dataset of sports news articles alongside a corresponding
sports ontology, the approach demonstrates practical improvements in summary quality,
supporting better decision-making and knowledge dissemination. The proposed model aims to
deliver domain-aware, fluent summaries that improve user experience in navigating
overwhelming volumes of news content.
