Text Ranking

Text ranking aims to order text units, such as sentences or documents, by relevance to a given query or context. Current research focuses on improving efficiency (e.g., through optimized algorithms like LexRank variants) and interpretability (e.g., by incorporating reasoning mechanisms into ranking models), often leveraging large language models (LLMs) like BERT and T5, sometimes adapted with specialized ranking losses. These advancements enhance information retrieval systems, enabling more accurate and explainable search results, and improving applications like automatic FAQ generation and summarization.