## 5 Conclusions and Future Work  
Inspired by how humans summarize the main content of multiple documents, this paper proposed a two-stage MDS method. First, topic-related paragraphs are extracted and sorted, and then the topic information is integrated into the summary generation process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **CR** & **PV** & **PT** & **O** \\ \hline T-DMCA & 17.6 & 2.8 & 2.3 & 3.0 \\ FT & 18.0 & 2.8 & 2.4 & 3.0 \\ HT & 18.1 & 2.9 & 2.4 & 2.8 \\ OurModel + TA + Datt & 18.3 & 3.0 & 2.4 & 3.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grammar checking comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **QA** & **Rating** \\ \hline Lead & 32.50 & \(-\)0.323 \\ HT & 55.05 & 0.243 \\ TG-MultiSum & 56.36 & 0.254 \\ OurModel + TA + Datt & 57.21 & 0.251 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System scores based on questions answered. The abstractive phase follows the transformer architecture and adds an external parser to the encoder. It analyzes the primary and secondary relationships between EDUs within the paragraphs and the hierarchical structure and relationships between paragraphs, generates a relationship matrix, and then uses complementary ways to generate richer semantic features. A topic attention module is introduced into the decoder to integrate topic information into the summary generation process to generate better summaries that meet human language expression habits and information acquisition needs. The experiments on the standard text summary dataset WikiSum showed that the ROUGE indicators of the summary model proposed in this article are better than the current best baseline models, and there are substantial improvements in summary syntax, semantics, and logical structure. In addition, our model highlights the essential principles of the design of artificial intelligence technology, focusing on combining theory and practice, profoundly exploring domain expertise, and strengthening the attention to the core elements. At the research level, we provide new research ideas for researchers and inspire future exploration of theoretical improvements such as MDS and NLP. At the application level, practitioners in fields such as news aggregation, intelligent search engine optimization, and extensive data analysis can use this model to improve the efficiency of information acquisition and processing. Although the experimental results confirm the significant impact of the TOMDS model on improving the relevance, flexibility, and accuracy of syntactic and semantic understanding in summary topics, we acknowledge that this method still has certain limitations. First, while the model performs well on the WikiSum dataset, further validation is necessary to assess its effectiveness in identifying and extracting topic-related paragraphs when dealing with multi-document sets featuring highly complex topic structures or rare topic content. Secondly, although the discourse parsing module was introduced to enhance contextual association modeling, addressing how to more accurately capture long-distance dependencies and balance global and local information remains an unresolved challenge. Additionally, when applying the topic attention mechanism, the effectiveness of generating summaries for topics that are not clearly defined or whose boundaries may be limited by the quality and diversity of predefined topic tags needs consideration. Therefore, the following research avenues can be pursued to advance this work: exploring more advanced transformer variants to improve the model's capacity for capturing discourse structures and topic relevance, and investigating ways to enable the model to effectively generate summaries in different fields through a minimal amount of annotated data, thereby strengthening its generalization capability and domain adaptability.