GeneSUM: Large Language Model-based Gene Summary Extraction

TL;DR

GeneSUM leverages large language models to automate and improve the extraction of concise, informative summaries of gene-related literature, addressing the challenge of rapidly expanding biomedical knowledge.

Contribution

This paper introduces GeneSUM, a novel two-stage framework using LLMs for efficient gene literature retrieval and summarization, advancing automated biomedical information extraction.

Findings

LLM significantly improves gene information integration

GeneSUM reduces redundancy in literature retrieval

Enhanced decision-making in gene research

Abstract

Emerging topics in biomedical research are continuously expanding, providing a wealth of information about genes and their function. This rapid proliferation of knowledge presents unprecedented opportunities for scientific discovery and formidable challenges for researchers striving to keep abreast of the latest advancements. One significant challenge is navigating the vast corpus of literature to extract vital gene-related information, a time-consuming and cumbersome task. To enhance the efficiency of this process, it is crucial to address several key challenges: (1) the overwhelming volume of literature, (2) the complexity of gene functions, and (3) the automated integration and generation. In response, we propose GeneSUM, a two-stage automated gene summary extractor utilizing a large language model (LLM). Our approach retrieves and eliminates redundancy of target gene literature and then fine-tunes the LLM to refine and streamline the summarization process. We conducted extensive experiments to validate the efficacy of our proposed framework. The results demonstrate that LLM significantly enhances the integration of gene-specific information, allowing more efficient decision-making in ongoing research.