# A Multitask Cross-Lingual Summary Method Based on ABO Mechanism
## 1 Introduction  
Cross-lingual summarization (CLS) involves extracting the core content of a document in one language and expressing it in another language [1]. This task requires addressing both redundant information and language differences, which usually involves multiple steps. However, decomposing cross-lingual text summarization into multiple tasks does not guarantee optimal results for each individual neural network. In addition, the distinct linguistic and syntactic structures of different languages need to be considered. Directly applying an end-to-end model across languages can result in the loss of important linguistic nuances and syntactic patterns, potentially leading to the omission of key information or the incorrect placement of sentences within the summary. Therefore, we must address two problems: how to integrate multiple tasks into an effective workflow [2], and how to capture the connections between different languages to generate summaries with reasonable content. To improve the quality of summaries, recent studies have focused on reducing variability between tasks [3; 4]. Transformer-based neural networks [5] have demonstrated the ability to share feature representations of languages between hidden layers in a language-independent way. Based on this idea, this paper abstracts the translation task into a cross-lingual summarization task with the same input sequence to output sequencelength ratio, and integrates it with the monolingual summarization (MS) task in the same model. The overall objective of the model is simplified to \"monolingual summarization task + cross-lingual summarization task\". The optimized cross-lingual pre-training model [6] is used as the basis, and the corpus with monolingual and cross-lingual summaries is used as input to allow all information to be shared between tasks. Integrating sequences helps the model gain a comprehensive understanding of the core content in the source language and facilitates better alignment of information across different languages. Furthermore, given the presence of unique nuances and expressions in each language, direct translation is often insufficient to capture these subtleties. By incorporating both monolingual and cross-lingual summary sequences, the model can effectively capture and comprehend these language-specific nuances. This becomes particularly crucial in the context of cross-language summarization, as it enables the model to capture the essence of the source document in its original language and generate a summary in the target language with efficiency. While end-to-end approaches are known for their improved generalization performance in handling noisy text inputs and reducing issues such as error accumulation [7], those based on pre-trained models often lack flexibility and fail to fully exploit the benefits of pre-training. Consequently, they frequently encounter overfitting problems during downstream task training. To tackle these challenges, we propose novel reinforced regularization methods. By introducing randomness during training, the reinforced regularization method effectively provides regularization, while the inclusion of sparse softmax encourages sparsity, preventing the model from excessively relying on specific features or classes. This combination further enhances the model's robustness. Given that the model integrates both monolingual and multilingual summarization tasks, it is necessary to configure the corresponding generation methods to enhance the quality of the generated summaries. However, many previous summarization approaches, such as top-k [8] or pointer networks [9], primarily focus on providing raw output content without ensuring the coherence of the generated statements. Unfortunately, these methods are insufficient for capturing semantic relationships between languages in cross-language summarization tasks. To address this limitation, we have devised the ABO mechanism.