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In the experimental tests, documents with zero correlation are also given rank

positions, (although, very low ones, in order that the normalized measures

may be calculated, and also so that precision/recall curves may be drawn

right up to 1.0 recall.)  A statement was made earlier suggesting that short

documents will present a barrier to perfect recall, because such short docu-

ment identifications are likely in some cases completely to miss something

important from the original full text of the document, thus resulting in

a zero match between the search request and such a relevant document.  Such

an occurrence will cause recall loss, and the resulting recall ceiling will

obviously be lower for short documents than long ones.  The data of Figure 8

give results comparing abstracts and titles in six tests.  The average recall

ceiling is computed by accepting only those relevant documents with some

positive correlation with the search requests; the recall ceiling with titles

is seen to go down to .66 in one case.   The recall ceiling for both abstracts

and titles would in practice be lower than the values given, since many users

would not be willing to examine all the documents with positive correlations

(this would involve examining in the ADI Abstracts Collection, on average,

70% of the total collection).  Comparing the results of Figure 8 with the

data in Figure 1, the greater length of the abstracts and titles on aerody-

namics over Documentation produces very slightly higher recall ceiling results,

but the average length abstracts and titles on computer science give quite

superior recall ceiling results.  The conclusion is that for users needing

high recall, titles only will not usually be adequate, and something nearer

abstract length is required.

        Since the results presented so far are all averages, and use the

arithmetic means over the request  sets, data are given in Figures 9, 10,

11, and 12 that are based on the individual requests and individual relevant