## 4 Summary  
This paper investigates the evaluation of presentation layout quality and automatic layout design. The TGAN automatic layout algorithm is implemented to optimize the \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Aesthetics** & **Functionality** & **Preference (\%)** \\ \hline LayoutGAN & 3.1 \(\pm\) 0.4 & 3.4 \(\pm\) 0.3 & 13\% \\ \hline Deep Layout & 3.6 \(\pm\) 0.3 & 3.8 \(\pm\) 0.2 & 9\% \\ \hline TGAN (Ours) & **4.3 \(\pm\) 0.2** & **4.5 \(\pm\) 0.3** & **78\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: User preference study results (mean scores with standard deviation; bold represents the best result). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Params (M)** & **FLOPs (G)** & **Training Time (h)** \\ \hline GAN (Baseline) & 12.5 & 4.2 & 48 \\ \hline LayoutTransformer & 68.3 & 23.7 & 120 \\ \hline VTN & 45.8 & 18.5 & 96 \\ \hline L-CGAN & 28.6 & 9.8 & 72 \\ \hline PLAY & 34.2 & 14.3 & 84 \\ \hline
**TGAN (Ours)** & **53.1** & **16.9** & **90** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of computational efficiency and model complexity (bold represents the best result). results of automatic layout. Subjective and objective evaluation scores are proposed, comprehensively assessing layout results in terms of alignment, balance, white space, scale, overlap, and boundary. In the experimental section, the ablation experiments confirm that directly optimizing layout quality metrics during training leads to more functionally coherent designs, particularly in complex multi-element scenarios. A comparison of evaluation results is conducted from both the aspects of visualized results and numerical assessments. The results indicate that the TGAN automatic layout algorithm achieves high subjective and objective evaluation scores, demonstrating aesthetically pleasing layouts that adhere to specific layout principles. Objects with constrained relationships are logically arranged on the page, facilitating efficient information transmission. The proposed algorithm in this paper demonstrates favorable outcomes for automatic page layout, with high-quality layout results. However, there is still room for further research:  
1. The dataset neglects the impact of internal elements and text on the overall layout. While the dataset used in this study provides a comprehensive representation of page layouts, it has certain limitations that may impact the results. Specifically, the dataset abstracts elements such as titles, subtitles, tables, graphics, and text into colored boxes, disregarding the internal content, text, images, and formats. This abstraction simplifies the problem but also omits critical information that could influence the overall layout quality. For instance, the relationships between text and images, such as how text wraps around images or how images are positioned relative to text blocks, are not captured in the dataset. These relationships are essential for creating visually appealing and functional layouts. Additionally, the dataset does not include information about the content of the text, such as font size, font type, and color, which can significantly affect readability and aesthetics. To enhance the quality of the algorithm, future work should consider incorporating more detailed information about internal elements into the dataset. This could include the following: Text and image relationships: including information about how text and images interact, such as text wrapping, image captions, and the spatial relationships between text blocks and images. Text content: Adding details about font size, font type, color, and other text attributes to better capture the visual and functional aspects of text in layouts. Image content: Including metadata about images, such as their aspect ratio, color palette, and content type (e.g., photograph, illustration, chart), to help the model understand how different types of images should be placed in a layout.