Keywords and keyphrases have many useful roles as document surrogates and descriptors, but the manual production of keyphrase metadata for large digital library collections is at best expensive and time-consuming, and at worst logistically impossible. Algorithms for keyphrase extraction like Kea and Extractor produce a set of phrases that are associated with a document. Though these sets are often utilized as a group, keyphrase extraction is usually evaluated by measuring the quality of individual keyphrases. This paper reports an assessment that asks human assessors to rate entire sets of keyphrases produced by Kea, Extractor and document authors. The results provide further evidence that human assessors rate all three sources highly (with some caveats), but show that the relationship between the quality of the phrases in a set and the set as a whole is not always simple. Choosing the best individual phrases will not necessarily produce the best set; combinations of lesser phrases may result in better overall quality.