XMSumm: A Unified Framework for Cross-Lingual and Multilingual Multi-Document Summarization

Summarization is an important task in the field of Natural Language Processing (NLP). With the widespread adoption of pre-trained language models, current summarization research has generally entered a stage dominated by abstractive summarization methods, and most of the research focuses on multi-document and cross-lingual summarization. Despite growing interest in both multi-document and cross-lingual summarization, existing research typically treats them as separate challenges, leaving the field of cross-lingual multi-document summarization blank. This gap is particularly significant in real-world applications. Taking news summarization, the main application field of summarization, as an example, international news reports are often not written in the reader's native language, and the same event may be reported in multiple languages. These real-world scenarios emphasize the growing need to develop models capable of cross-lingual summarization, and more broadly, multilingual multi-document summarization. In this work, we propose an end-to-end approach capable of handling both cross-lingual and multilingual multi-document summarization tasks. We validate the effectiveness of our approach through experiments on both self-built datasets and public datasets.