Multi Granularity Summarization

Multi-granularity summarization focuses on generating summaries of varying lengths and levels of detail, catering to diverse user needs and preferences. Recent research emphasizes unsupervised approaches, leveraging techniques like event-based salience ranking and graph-based methods (e.g., utilizing citation networks in scientific papers) to extract or generate summaries at different granularities. These advancements improve the customization and efficiency of summarization, particularly beneficial for handling large volumes of scientific literature or other complex text corpora. The development of benchmark datasets and improved model architectures are driving progress in this field.