Observing high rates of read duplicates in RNA-seq libraries is common. It may not be an indication of poor library complexity caused by low sample input or over-amplification. It might be caused by such problems but it is often because of very high abundance of a small number of genes (usually ribosomal or mitochondrial house keeping genes). For example, I have seen libraries where ~60% of all reads mapped to the 2-10 most highly expressed genes. Sometimes 75% of all reads map to the top 0.1% of expressed genes. The result of such heavy sampling of these genes is a high number of duplicate reads (even when considering read pairs in assessing duplicates).

This is not necessarily an artifact. Some RNA samples really do have such a skewed distribution of expression values. If the highly expressed genes are non-coding RNA genes, an efficient polyA selection before library construction will often dramatically improve the situation. Other options include a variety of library normalization techniques or simply ignoring the phenomenon. Since you have a finite number of reads in the library, if you are burning most of them on a small number of highly abundant genes you may need to sequence deeper to get good representation of the rest of the transcriptome...

Some RNA-seq software will consume vast quantities of memory when such genes are encountered. For practical reasons some filtering of the data may be required to remove highly represented sequences.