## 4 Experimental Results and Comparative Analyses
### Performance Evaluations
#### 4.2.1 Comparisons of LLM-Based Pre-Processing Techniques
In the ISAC field, the TD value increased from 0.6775 to 0.7537,  
Figure 4: TC (left-hand side panels: (**a**) baseline, (**c**) baseline + long-sum, and (**e**) baseline + short-sum) values and TD (right-hand side panels: (**b**) baseline, (**d**) baseline + long-sum, and (**f**) baseline + short-sum) values over the years for different topic modeling approaches. The baseline represents the baseline model, while the long and short models are our LLM-based summarization techniques with long-and short-length summaries, respectively, illustrating the performance trends in TC and TD metrics over the years. and in the EAI field, it rose from 0.6647 to 0.7400. This clearly shows that short summarization, which focuses on key point extraction, is highly effective for density-related tasks. In contrast, the 'Baseline + long-sum' approach also improved the TD performance in some fields like EAIs, increasing from 0.6647 to 0.6983. However, the overall improvement was more pronounced with the short-sum method. In the Supplementary Materials, we included one folder named 'Heatmaps' to comprehensively visualize the performance of different embedding methods. These visualizations are presented for each dataset under three conditions: the baseline model, the baseline with short summarization (baseline + short sum), and the baseline with long summarization (baseline + long sum). The heatmaps specifically focus on cosine similarity, which is a crucial metric for evaluating the embedding quality. By comparing the cosine similarity heatmaps across these three scenarios, we can clearly observe the differences in how well each approach captures semantic relationships within the patent data. For example, we can observe how the long summarization might preserve more detailed semantic information, leading to higher cosine similarity values in relevant regions of the heatmap compared to the baseline method. This provides an intuitive understanding of the impact of different summarization strategies and the baseline model on the quality of embeddings, further validating the effectiveness of our proposed methods in handling 6G patent data. Additionally, to further validate the effectiveness of our LLM-based pre-processing methods, we conducted additional experiments and included 2D visualizations of the embeddings for each dataset in the Supplementary Materials named 'Clusters'. We utilized UMAP, a powerful dimensionality reduction technique, to project the high-dimensional embeddings into a 2D space for better visualization. The generated HTML-based interactive visualizations offer an intuitive understanding of the impact of LLM-based pre-processing on the patent data. In these visualizations, we can clearly observe the distribution of data points. When LLM-based pre-processing was applied, the data points tended to cluster more tightly, indicating a reduction in noise. For instance, in the RIS dataset, the pre-processed data (both the long summarization approach and short summarization approach) show a more organized structure, with data points forming distinct groups. This suggests that LLM-based summarization effectively filters out irrelevant information, enabling the model to better capture the underlying semantic relationships within the patent data. These visualizations serve as evidence for the noise-reducing capabilities of our LLM-based pre-processing approach, complementing our overall analysis of the dynamic topic modeling framework for 6G patent data. In summary, our research validates that different LLM-based summarization strategies have specific and predictable impacts on 6G-related task performances. The long-summarization technique enhances the overall performance compared to the baseline, making it a more fitting choice for complex analysis tasks that demand in-depth understanding and comprehensive interpretation.