A LLM-Powered Approach for Generating Meeting Minutes from Audio Conversations

Title

A LLM-Powered Approach for Generating Meeting Minutes from Audio Conversations

Creator

Mohanty, Aniruddha; Prusty, Alok Ranjan; Porwal, Purva

Description

Accurate documentation of meetings is essential for effective communication, decision-making, and accountability across various settings, including corporate environments, academic discussions, research collaborations, project reviews, and multi-stakeholder forums. Manual note-taking is often error-prone and time-consuming, highlighting the need for automated solutions. This paper proposes an LLM-based approach for generating structured Minutes of Meeting (MoM) from audio recordings. The method begins with audio transcription using the Vosk model, followed by grammatical and punctuation recovery to enhance text readability. The preprocessing step cleans and segments the transcribed text. Named Entity Recognition (NER) is applied to identify relevant entities such as names, dates, and organizations. The method then segments the conversation into topical sections aligned with the meeting agenda. A Large Language Model (LLM), named OpenAI, has been used to produce abstractive summaries. An action item extraction module identifies tasks, responsible individuals, and deadlines. A speaker analysis component highlights principal participants in decision-making processes. The system organizes the final output into a structured format, such as JSON or PDF, for easy access and distribution. This end-to-end approach enhances the clarity, accessibility, and utility of meeting documentation, reducing manual effort and improving organizational productivity. 2025 IEEE.

Source

2025 IEEE International Conference on Recent Advances in Computing and Systems, ReACS 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Action items; LLM; MoM; Named Entity Recognition (NER); Summary; Vosk

Coverage

Mohanty A., Christ (DEEMED To Be UNIVERSITY), Department of Computer Science and Engineering, Bangalore, 560074, India; Prusty A.R., National Skill Training Institute (NSTI), Bhubaneswar, 751010, India; Porwal P., Mahakal Institute of Technology, Ujjain, 456001, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833154927-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/REACS67479.2025.11413427

https://www.scopus.com/pages/publications/105036523927?origin=resultslist