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 · 2023
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.