From Keywords to Structured Summaries: Streamlining Scholarly Information Access
It addresses the problem of information overload for researchers by streamlining access to publications, though it appears incremental as it builds on existing IR and LLM technologies.
This paper tackles the inefficiency of keyword-based search engines for scholarly information by proposing a system that uses structured records and visualization dashboards, demonstrated through a proof-of-concept for reproductive number estimates with an automated LLM-based approach.
This paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications. The proposed solution involves structured records, underpinning advanced information technology (IT) tools, including visualization dashboards, to revolutionize how researchers access and filter articles, replacing the traditional text-heavy approach. This vision is exemplified through a proof of concept centered on the "reproductive number estimate of infectious diseases" research theme, using a fine-tuned large language model (LLM) to automate the creation of structured records to populate a backend database that now goes beyond keywords. The result is a next-generation information access system as an IR method accessible at https://orkg.org/usecases/r0-estimates.