The linguistic summarization of a dataset is a process whose complexity depends linearly on the size of the dataset and exponentially on the size of the fuzzy vocabulary. To efficiently summarize large datasets stored in Relational DataBases, reliable estimated cardinalities can be derived from statistics about the data distribution maintained by the RDB Management System, with no expensive data scans. This paper proposes to improve the precision of such estimated summaries while preserving their efficiency, by enriching the statistics-based approach with local scan-based corrections when needed: the proposed FRELS method provides efficient strategies both for identifying the needs and performing the corrections. Experiments conducted on real data show that FRELS remains incomparably more efficient than data-scan-based approaches to data summarization and offers a better precision than purely statistics-based approaches. The generation of estimated linguistic summaries takes a couple of seconds, even for datasets containing millions of tuples, with a reliability of more than 95%.