Leveraging LLMs for Scientific Abstract Summarization: Unearthing the Essence of Research in a Single Sentence

There are lots of scientific articles are being published every year, it is increasingly challenging for researchers to maintain oversight and track scientific progress. Meanwhile, Large Language Models (LLMs) have revolutionized natural language processing tasks. This research focuses on generating summaries from research paper abstracts by utilizing LLMs and comprehensively evaluating the performance of the summarization. LLMs offer customizable outputs through Prompt Engineering by leveraging descriptive instructions including instructive examples and injection of context knowledge. We investigate the performance of various prompting techniques for various LLMs using both GPT-4 and human evaluation. For that purpose, we created a comprehensive benchmark dataset for scholarly summarization covering multiple scientific domains. We integrated our approach in the Open Research Knowledge Graph (ORKG) to enable quicker syn-Thesis of research findings and trends across multiple studies, facilitating the dissemination of scientific knowledge to policymakers, practitioners, and the public.