###### Abstract  
In a multi-document summarization task, if the user can decide on the summary topic, the generated summary can better align with the reader's specific needs and preferences. This paper addresses the issue of overly general content generation by common multi-document summarization models and proposes a topic-oriented multi-document summarization (TOMDS) approach. The method is divided into two stages: extraction and abstraction. During the extractive stage, it primarily identifies and retrieves paragraphs relevant to the designated topic, subsequently sorting them based on their relevance to the topic and forming an initial subset of documents. In the abstractive stage, building upon the transformer architecture, the process includes two parts: encoding and decoding. In the encoding part, we integrated an external discourse parsing module that focuses on both micro-level within-paragraph semantic relationships and macro-level inter-paragraph connections, effectively combining these with the implicit relationships in the source document to produce more enriched semantic features. In the decoding part, we incorporated a topic-aware attention mechanism that dynamically zeroes in on information pertinent to the chosen topic, thus guiding the summary generation process more effectively. The proposed model was primarily evaluated using the standard text summary dataset WikiSum. The experimental results show that our model significantly enhanced the thematic relevance and flexibility of the summaries and improved the accuracy of grammatical and semantic comprehension in the generated summaries. multi-document summarization; topic-oriented; extractive; abstractive; attention mechanism; discourse analysis Article  
19 January 202417 February 202417 February  
and research. Much research has been carried out on algorithm technology, datasets, evaluation indicators, and systematic text summarization technology. Nevertheless, in practical applications, the summarization of a single document's information falls short of fulfilling people's information acquisition requirements. Therefore, NLP became one possible way to empower multi-document summarization (MDS) technology. [PERSON] [2] emphasized the importance of high-quality data for training complex deep learning models. Due to the lack of large-scale datasets in MDS and the significant increase in computing resources and storage requirements when processing long texts, the research progress was once limited. It was not until 2018 when [PERSON] proposed a large-scale automatically generated auto-hMDS corpus [3], and the large-scale datasets WikiSum [4] and Multi-News [5] came out one after the other, that multi-document summary technology was widely studied and applied. The existing MDS technology [6; 7; 8; 9] usually refines and compresses the information of multiple documents under the same topic and presents the primary information in summary form. However, multiple input documents do not necessarily contain only one topic but usually have complex and diverse topic structures in practical applications. If all topics are considered, the critical information of the summary may be overlooked, and the key points cannot be highlighted. Based on the above problems, we propose a topic-oriented two-stage MDS model, which enables users the right to choose summary topics and achieves more personalized information extraction. It meets users' information acquisition needs, improves the information acquisition efficiency, and promotes user participation and the possibility of customized summary generation. We conducted an experiment on the WikiSum dataset. The experimental results demonstrate the effectiveness and superiority of our model. To sum up, the contributions of this paper are as follows:  
1. We incorporated and emphasized topic information in the extraction and generation phases of the model to further enhance the focus of multi-document summaries on specific topics. 2.