A Poisson convolution model for characterizing topical content with word frequency and exclusivity
Table 6: Proportion of unique words in topic summaries obtained with different strategies
['(a) 5-word summaries N topics', '(a) 5-word summaries 10', '(a) 5-word summaries 25', '(a) 5-word summaries 50', '(a) 5-word summaries 100']
One simple metric for quantifying the diversity between topic summaries is the proportion of unique words across all the summaries produced from a model fit. For example, five-word summaries from a 100-topic model would have at most 500 unique words, and the proportion of the total achieved is an indication for whether the word lists are presenting diverse information. (LDA FREX in the table), and ranking words by frequency using LDA word rate estimates (LDA FREQ in the table).