We normalizeR byQ The interplay between the two types of data is encoded in the following regularizing constraint: Full Objective Function.
the full objective function consists of the three parts discussed above: Furthermore, using the following notation, we can show a closed form solution to Equation 4 as follows: Basic Features Social Features -num of words -avg/sum frequency of -absolute/relative position words appearing in comment -overlaps with headline -avg/sum frequency of -avg/sum TF-IDF scores dependency relations -num of NEs appearing in comment -TF/TF-IDF simi with comments -contains URL -JS/KL divergence (div) with article -user rating (pos/neg) -JS/KL div with comments Sentiment Features -num /proportion of positive/negative/neutral words (MPQA (Wilson et al., 2005), General Inquirer (Stone et al., 1966)) -num /proportion of sentiment words  Now we present an optimization framework for timeline generation.