Approaches for Summarization of Articles Across Multiple Languages: A Review

Multilingual text summarization requires efficient solutions because global content continues to increase in quantity. The paper provides an overview of contemporary field advancements that derive from the fundamental literature. Advanced scoring models and transformer models alongside domain-specific pretrained models demonstrate notable success with their implementations. Content extraction has benefited from PageRank-based approaches and word embedding techniques and new datasets have enabled abstraction summarization in languages including Malayalam and Hindi. The combination of rules with neural networks produces effective solutions for domains such as biomedical fields and legal document aggregation. New knowledge-enabled transformers expand summarization capacities through integration between discourse relations and entity-awareness capabilities. Progress has been made in managing language diversity, but the field continues to encounter difficulties with preserving semantic value and the cultural basis of underrepresented languages. The analyzed research indicates that achieving breakthroughs requires specialized datasets together with strong evaluation tools and model-based innovations that address ongoing challenges. The evaluation demonstrates how multilingual summary technology enables better accessibility across educational settings and journalism fields and social media platforms. This study develops multilingual summary technology by conducting state-of-the-art methodological investigations and identifying existing research gaps within global information inclusivity.