# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 6 Conclusions and Future Work  
This work introduced the ECNN-ASCD solution to detect writing style changes in multi-authored Arabic documents. ECNN-ASCD is based on the EdgeConv module, which integrates edge representations during the message-passing scheme. Edges in ECNN-ASCD indicate text boundaries, and their representations are adjusted across layers while preserving text representations surrounding these boundaries. The performance of ECNN-ASCD was evaluated on AraSCD, the first Arabic dataset for the SCD task Appendix A. Through intensive experiments, ECNN-ASCD demonstrated significantly superior performance over comparative models and achieved the best \(F_{1}\) score of 0.9945%, 0.9381%, and 0.9120% on easy, medium, and hard instances, respectively. These results show the enhancement of boundary representations by aggregating initial node and learnable edge representations extracted from previous layers. The findings of the ablation experiments statistically validate the effectiveness of ECNN-ASCD components in improving the performance of detecting style changes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **\# Doc.** & **Avg. Doc. Leng.** & **Avg. Para. Leng.** & **Avg. Style Change** \\ \hline PAN 2021 (Task 2) & 16,000 & 6.8836 para. & 44.2091 words & 3.1556 changes (53.63\%) \\ PAN 2022 (Task 3) & 10,000 & 15.9652 para. & 19.9412 words & 8.1380 changes (54.38\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Statistics of PAN datasets. \begin{table}
\begin{tabular}{c c|c c} \hline \hline
**PAN 2021-Task 2** & \multicolumn{3}{c}{**PAN 2022-Task 3**} \\
**Solution** & \(F_{1}\) & **Solution** & \(F_{1}\) \\ \hline [PERSON] et al. [97] & 0.7750 & [PERSON] et al. [77] & 0.7156 \\ [PERSON] et al. [76] & 0.7510 & [PERSON] et al. [79] & 0.6720 \\ ECNN-ESCD & 0.7373 & [PERSON] et al. [75] & 0.6581 \\ [PERSON] [78] & 0.7070 & [PERSON] and [PERSON] [63] & 0.6483 \\ [PERSON] et al. [110] & 0.7043 & [PERSON] et al. [62] & 0.6314 \\ [PERSON] and [PERSON] [55] & 0.6690 & [PERSON] et al. [110] & 0.6312 \\ [PERSON] et al. [43] & 0.6570 & ECNN-ESCD & 0.6215 \\ [PERSON] [52] & 0.6470 & [PERSON] et al. [40] & 0.5636 \\ - & - & [PERSON] and & 0.5565 \\ - & - & [PERSON] [67] & 0.5565 \\ - & - & [PERSON] and [PERSON] [10] & 0.4995 \\ \hline Baseline (random) & 0.470 & Baseline (random) & 0.4809 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: SOTA results on PAN 2021 and PAN 2022 datasets. As the first publicly available solution for the Arabic SCD, ECNN-ASCD would bridge the gap in the Arabic SCD task and pave the way for more active research. Since the message-passing scheme is crucial in the GNN modules, adjusting it in EdgeConv for the SCD could detect more diverse writing styles. Designing a richer graph, such as incorporating dependency trees and knowledge graphs, could involve more representative edge features. Further enhancements could also be conducted by investigating attention mechanisms. A deep study of these mechanisms can improve detection by focusing on the relevant parts of the written texts. Future investigations are recommended to design specialized SCD datasets that incorporate both physiological and psychological aspects, such as annotations of sentiment analysis tasks, to gain deeper insights into studying these aspects. Evaluating the models using human assessment can provide a comprehensive understanding of their performance across multiple perspectives, facilitating further improvements. Exploring model interpretability presents a promising direction in the future, which could enhance the applicability of ECNN-ASCD across various domains. Conceptualization, A.S.A. ; methodology, A.S.A. and M.E.B.M. ; software, A.S.A. ; validation, A.S.A. and M.E.B.M. ; formal analysis, A.S.A. ; investigation, A.S.A. ; resources, A.S.A. ; data curation, A.S.A. ; writing--original draft preparation, A.S.A. ; writing--review and editing, A.S.A. and M.E.B.M. ; visualization, A.S.A. ; supervision, M.E.B.M. ; project administration, A.S.A. and M.E.B.M. ; funding acquisition, A.S.A.