Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective
Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN
Abstract
This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, namely, GPT-4, GPT-3.5, PaLM-2, and LLaMA-2. Our findings reveal that most closed-source LLMs are generally better in terms of performance. However, much smaller open-source models like LLaMA-2 (7B and 13B) could still achieve performance comparable to the large closed-source models even in zero-shot scenarios. Considering the privacy concerns of closed-source models for only being accessible via API, alongside the high cost associated with using fine-tuned versions of the closed-source models, the opensource models that can achieve competitive performance are more advantageous for industrial use. Balancing performance with associated costs and privacy concerns, the LLaMA-2-7B model looks more promising for industrial usage. In sum, this paper offers practical insights on using LLMs for real-world business meeting summarization, shedding light on the trade-offs between performance and cost.- Anthology ID:
- 2023.emnlp-industry.33
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 343–352
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-industry.33
- DOI:
- 10.18653/v1/2023.emnlp-industry.33
- Cite (ACL):
- Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, and Shashi Bhushan TN. 2023. Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 343–352, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective (Laskar et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-industry.33.pdf