## 4 Experimental Results and Comparative Analyses
### Performance Evaluations
#### 4.2.1 Comparisons of LLM-Based Pre-Processing Techniques  
In our research on 6G patent data, the primary objective was to understand how different summarization strategies impact the performance of various tasks related to 6G technological analysis. We collected patent datasets from five emerging fields in 6G from 2016 to 2024. To process this large volume of patent data, we employed ChatGLM, an online API-based LLM protocol. It was selected for its ability to seamlessly parallel process the patent context, making it more cost-effective and efficient than local LLMs. For content summarization, we adopted long and short summarization strategies with different constraints on the number of words, which were 15-30 and 30-60, respectively. Additionally, we used refined prompts as proposed in Section 3.1.2 to further improve the results and prevent irrelevant information in the topic extractions. We adopted the all-MiniLM-L6-v2, a typical SBERT model, as the default baseline setting in this experiment. We calculated the TC and TD metrics for each period at one-year intervals throughout the entire duration. The values reported for both TC and TD are the average values calculated across all the years. From the data presented in Table 2 and Figure 4, it is evident that the application of our proposed summarization techniques, when combined with our LLM-based pre-processing, has distinct effects on the performance metrics of TC and TD across different datasets consistently. For the TC metric in Table 2, compared to the baseline, the 'Baseline + long-sum' approach shows an improvement in most fields in five datasets. For instance, in the NST field, the TC value increased from 0.4900 to 0.5039, indicating that the long summarization (a key element of the 'Baseline + long - sum' approach, characterized by its ability to preserve detailed information) indeed helps in more complex analysis tasks. Similarly, in the ISAC field, the value rose from 0.4749 to 0.5024. This improvement is consistent with our previous analysis that long summarization are beneficial for exploring complex relationships within 6G technological concepts. However, the 'Baseline + short-sum' approach led to a decrease in TC performance across all fields. This performance decrease is expected as short summarization (a key element of the 'Baseline + short - sum' approach) sacrifice detailed information crucial for complexity-related tasks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Metrics** & **NSTs** & **RISs** & **ISAC** & **ISAGSC** & **EAIs** \\ \hline \multirow{2}{*}{Baseline} & TC & 0.4900 & 0.4978 & 0.4749 & 0.4683 & 0.4615 \\  & TD & 0.5968 & 0.6849 & 0.6775 & 0.6542 & 0.6647 \\ \hline Baseline + & TC & 0.5039 & 0.5002 & 0.5024 & 0.4883 & 0.4717 \\ long sum & TD & 0.6157 & 0.6985 & 0.6923 & 0.6643 & 0.6983 \\ \hline Baseline + & TC & 0.4005 & 0.4314 & 0.4140 & 0.4062 & 0.4032 \\ short sum & TD & 0.6656 & 0.7159 & 0.7537 & 0.6923 & 0.7400 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: This table compares the performance of annual TC and TD averages in DTM (From 2016 to 2024). We compare the baseline to our LLM-based pre-processing techniques with long and short summarization techniques. The elements highlighted in red in the table represent the maximum values in the corresponding columns. The baseline model's TC curve in Figure 4 shows a trend over the years, potentially reflecting the baseline performance in capturing topic-related characteristics. The long-sum and short-sum models, which are based on BERTopic with different summary lengths, exhibit distinct TC patterns. These differences might indicate that the summary length impacts how well the model can represent topics in terms of TC, with variations in the curves suggesting different levels of effectiveness or adaptability to the data over time. Regarding the TD metric in Table 2, the 'Baseline + short-sum' approach demonstrates significant enhancements.