Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mohanty, Aniruddha; Prusty, Alok Ranjan; Porwal, Purva Title A name given to the resource A LLM-Powered Approach for Generating Meeting Minutes from Audio Conversations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Recent Advances in Computing and Systems, ReACS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/REACS67479.2025.11413427" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/REACS67479.2025.11413427</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036523927?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036523927?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Action items; LLM; MoM; Named Entity Recognition (NER); Summary; Vosk Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154927-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online