# An Abstract Summarization Method Combining Global Topics
## 6 Analysis and Discussion  
### Effect of Number of Topics  
To illustrate the effect of introducing topic information into ACGT, we analyze the correlation between the number of topics and summary quality.  
When training the LDA, the number of topics in the document set needs to be determined. [PERSON] [23], the proposer of LDA, proposed perplexity as the metric; he suggested that a probability distribution model with low perplexity can predict the original text better. Thus, for CNN/DM dataset, we take the top 10 words with the highest probability for each topic as features and calculate the perplexity value of the LDA model for the number of topics between 1 and 50. The results are shown in Figure 7a. It shows that the average perplexity value of the model is lower when the number of topics is 40-45. Using PGEN + Cov as the baseline method, the ROUGE metrics of ACGT generating summaries when the number of topics is from 1 to 50 are shown in Figure 7b-d. We find that the difference between the ROUGE scores of the ACGT and the baseline method increases significantly as the perplexity value decreases. When the number of topics is 43, the perplexity value is the lowest, at which time the ROUGE difference is the largest, and the performance of ACGT to generate summaries is optimal.  
In the LCSTS dataset, the top 10 lexical items with the highest probability in each topic are also chosen as features, and the perplexity is calculated for the number of topics between 1 and 30, and the results are shown in Figure 8a. It shows that the model has the lowest average perplexity when the number of topics is 18 to 20, and overfitting occurs when the number of topics is greater than 20. When the number of topics ranges from 1 to 20, the ROUGE reference values of ACGT generating summaries compared with the baseline methods are shown in Figure 8b-d. It can be demonstrated that the perplexity and the ROUGE difference are negatively correlated. When the number of topics is 18, the ROUGE difference is the best and the performance of ACGT to generate summaries is optimal.  
The experiments show that the performance of the ACGT for generating summaries changes significantly with the number of topics introduced. When the perplexity of LDA decreases, the performance of ACGT generating summaries improves, indicating that the topic information extracted by LDA has a positive impact on the performance of summaries, and the ACGT is effective in incorporating topic information into summary generation. The number of topics used by CNN/DM is 43 and LCSTS is 18 in this experiment.  
Figure 6: Correlation between summaries quality and the number of topic terms in the LCSTS dataset (number of topics is 18).  
Figure 8: Correlation of summary quality with the number of topics in LCSTS dataset, where, figure (**a**) shows the variation of perplexity with the number of topics.In figure (**b–d**), the vertical axis represents the ROUGE difference between ACGT and the baseline model [4].  
Figure 7: Correlation of summary quality with the number of topics in CNN/DM dataset, where, figure (**a**) shows the variation of perplexity with the number of topics. In figure(**b–d**) the vertical axis represents the ROUGE difference between ACGT and the baseline model [4].