Unlocking Scholarly Article Insights: Creating a Scholarly Article Content Extraction Tool –Objectives, Methodology, and a Comparative Analysis of Summary Results

We developed an intuitive prototype scholarly article content extraction tool to help individuals and students extract key content from scholarly articles. Although scholarly articles typically include an abstract, they often lack depth. Existing summarization tools are not always user-friendly and often require setup and learning processes. Most existing tools are designed for the information industry to extract metadata and key content efficiently. This paper introduces our innovative Scholarly Article Content Extraction Tool (SACET), which extracts content from a single scholarly article and instantly returns an accurate natural language summary. SACET is accessible anytime/anywhere without setup or extensive learning. We compare SACET's summaries with those from ChatGPT, highlighting the differences, value, and uniqueness of our innovation. Publishing our innovation is crucial due to its potential to advance content extraction applications. This article discusses the objectives, architecture, content extraction methodology, underlying algorithms, operational workflow, and additional work needed to improve the tool.