Abstract Generation with Hybrid Model Supported by Relevance Matrix

In the face of the ever-fast-paced development of natural language processing, the need for the ability to condense information into coherent and concise abstracts is becoming irreplaceable. This study introduces a GPT-Neo-based hybrid model that leverages a relevance matrix for improved summarization of research papers and also stands out for its remarkable resource efficiency. Compared to the most up-to-date state-of-the-art solutions, our model provides a competitive level of performance utilizing minimum computational resources. This efficiency expands the scope of application for such technologies in resource-constrained environments which otherwise would not have been feasible, making them more accessible while eliminating environmental and economic costs. Through detailed methodology and performance evaluation, primarily using ROUGE scores, we show the model’s unique place in keeping a good balance between performance and steadiness. The outcome of our research supports a wider view of NLP development directed at both increased efficiency and accessibility in addition to improvement of the algorithms.