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Beyond bibliometrics, there is interest in characterizing the evolution ofthe number of ideas in scientific papers. A common approach for investigatingthis involves analyzing the titles of publications to detect vocabulary changesover time. With the notion that phrases, or more specifically keyphrases,represent concepts, lexical diversity metrics are applied to phrased versionsof the titles. Thus changes in lexical diversity are treated as indicators ofshifts, and possibly expansion, of research. Therefore, optimizing detection ofkeyphrases is an important aspect of this process. Rather than just one, wepropose to use multiple phrase detection models with the goal to produce a morecomprehensive set of keyphrases from the source corpora. Another potentialadvantage to this approach is that the union and difference of these sets mayprovide automated techniques for identifying and omitting non-specific phrases.We compare the performance of several phrase detection models, analyze thekeyphrase sets output of each, and calculate lexical diversity of corporavariants incorporating keyphrases from each model, using four common lexicaldiversity metrics.

Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles