AUTOMATIC MINUTES SUMMARIZATION IN INDICO USING AN OPEN-SOURCE LARGE LANGUAGE MODEL

This project presents a plugin developed for Indico, CERN’s open-source event management platform, designed to automatically summarize meeting minutes using large language models (LLMs). The goal is to reduce the manual effort of post-meeting documentation by leveraging open-source, efficient, and accurate AI models. The plugin integrates directly into the Indico interface, enabling users to generate structured summaries by selecting meetings, editing prompts, and receiving results. A range of open-source LLMs were evaluated, from lightweight models to large quantized ones, based on output quality, accuracy, inference time, and consistency. While smaller models proved insufficient, more powerful quantized models deployed via llama.cpp and eventually a GPU-backed model on CERN’s Kubeflow infrastructure achieved significantly better results. Human evaluations were used to assess summary quality and inform model selection. The outcome is a working prototype capable of producing clear and concise summaries, laying the foundation for future improvements such as automatic metric-based evaluation, support for more complex prompts, and context-aware summarization.