Cross-language summarization (CLS) aims to summarize and summarize the core content of the text in the source language (e.g., Burmese) with the text in the target language (e.g., Chinese). CLS is essentially a joint task of machine translation and monolingual summarization, which requires the model to have the capabilities of both aspects. In low-resource language scenarios such as Vietnamese and Burmese, cross-language summary training data is scarce, and Chinese, Burmese, and Vietnamese belong to different language families, and the language differences are large, resulting in the current cross-language summary method being less generalizable. Difference. Based on this, taking Burmese-Chinese and Vietnamese-Chinese as the research objects, a cross-language summary method with language relationship enhancement is proposed. This method first converts the input sequence into continuous word pairs, and then calculates the relationship between the source language and the target language. The relationship between these consecutive word pairs; finally, a joint training method of machine translation and monolingual summarization is introduced to effectively capture the relationship between the target language and the source language, improving the model's generalization and processing capabilities for continuous text. Extensive experiments were conducted on self-built data sets. Compared with other baseline models, the method proposed in the study improved the ROUGE-1, ROUGE-2 and ROUGE-L evaluation indicators by 5%, 1% and 4% respectively.