Our own implementation of an MMR-based baseline uses an existing algorithm to maximize the following summary set score function (Lin and Bilmes, 2010): where v D is the vector representing the source documents, v Q is the vector representing the query terms, Sim is the cosine similarity, and γ is a parameter.
the first term of this function reflects how the sentences reflect the entire documents; the second term reflects the relevance of the sentences to the query; and finally the function penalizes redundant sentences.