For example, the Jensen Shannon divergence (JS divergence), an information-theoretic measure not relying on human-written summaries, compares system summaries with source documents regarding their underlying probability distribution of n-grams (Lin et al., 2006).
unlike ROUGE, JS divergence is neither submodular nor factorizable (see, e.g., Louis and Nenkova (2013)), and consequently can not be optimized via ILP or submodular function optimization.