SVR model aims to learn a function f : R n → R, which will be used to predict the content evaluation score for each summary y ∈ R given a feature vector X ∈ R n .
given l training instances (X 1 , y 1 ), ..., (X l , y l ), the SVR model is learnt by solving the following optimization problem (Vapnik, 1999); W is a vector of feature weights; φ is a function that maps feature vectors to a new vector space of higher dimensionality to allow non-linear functions to be learnt in the original space; C > 0 and > 0 are given.