Then, a similarity matrix S ∈ R m×n is calculated for a given article representation, S ∈ R m×d , and a template representation, T ∈ R n×d : where f is the similarity function, and the common options for f include: Most previous work uses dot product or bilinear function (Chen et al., 2016) for the similarity, yet we find the family of Euclidean distance perform much better for our task.
to examine the effect of this module, we studied its ranking quality under different ranges as in Section 4.1.