LLM_Engineering_OLD/community-contributions/biomedical-article-summariser/README.md

Biomedical Article Abstract Summariser using Europe PMC + Ollama

This is a simple app that demonstrates an article abstract summariser leveraging Europe PMC’s API and Ollama LLMs to generate concise summaries of biomedical literature.

🔍 About Europe PMC (EPMC)

Europe PMC is a free, open-access database that provides access to millions of life sciences and biomedical articles, research papers, and preprints. It is part of the PubMed Central International (PMCI) network.

Features

This solution presents 2 methods:

1. A simple demo via a jupyter notebook
2. An interactive demo via gradio, running on your local computer.

Core Features:

• Fetch an article’s metadata and abstract via Europe PMC’s API (using a provided PMCID).
• Preprocess and clean the abstract text unnecessary tags e.g referenc tag or math formula.
• Summarise abstracts into bullet points + a short paragraph using Ollama models.

📌 How to Use

• Go to Europe PMC' website.

• Use the search bar to find an open-access article by keywords, entity names, journal, or author. E.g Genes, Diseases, nutrition etc

• Since the app currently only runs on open-access only articles, you'll need to restrict results to open-access only articles: add filters like HAS_FT:Y or IN_EPMC:Y to your search syntax. E.g ."Genes: HAS_FT:Y"

• Select your article of interest and copy its PMCID (e.g., PMC1234567).

• Run the summariser:

  • via notebook: Paste the PMCID as a string in the display_response func, after running all other cells.
  • via gradio:
    • run the python script via CLI:

    python article_summariser-gradio.py
    

    • Paste the PMCID as you've copied it in the Enter a **EuropePMC Article ID textbox.
    • click on the Fetch Article Abstract and generate Summary button. N.B: I've observed that using llama3.2 runs faster on my pc. You may experience some delays with all other models. Also make sure to already have ollama running via ollama serve on your terminal before running the script.