Finally, the keywords S ⊆ t, with |S| ≤ k, are chosen by maximizing the cumulative reward function over all the topics, formulated as follows: Since R(S) is submodular, the greedy algorithm for maximizing R(S) is shown as Algorithm 1 on the next page, with r {w},z being similar to r S,z with S = {w}.
if λ = 1, the reward function is linear and only measures the topical similarity of words with the main topics of t. when 0 < λ < 1, as soon as a word is selected from a topic, other words from the same topic start having diminishing gains.