Cross Lingual Summarization

Cross-lingual summarization (CLS) tackles the challenge of automatically generating summaries of documents in one language from their counterparts in another. Current research emphasizes improving the accuracy and efficiency of CLS, focusing on approaches like "translate-then-summarize" pipelines and end-to-end models, often leveraging large language models (LLMs) and contrastive learning techniques to address data scarcity issues, particularly for low-resource languages. This field is crucial for bridging language barriers in information access and dissemination, with applications ranging from news translation to scientific literature review and legal document processing. The development of more robust and efficient CLS systems is driving advancements in both multilingual natural language processing and cross-lingual understanding.