# Difference between dynamic pooling and static pooling in convolutional neural networks

Since yesterday I was thinking that pooling layer in CNN has fixed size(e.g. 2 by 2). Then I saw in this paper: http://phd.nal.co/papers/Kalchbrenner_DCNN_ACL14

We define a convolutional neural network architecture and apply it to the semantic modelling of sentences. The network handles input sequences of varying length. The layers in the network interleave one-dimensional convolutional layers and dynamic k-max pooling layers. Dynamic k-max pooling is a generalisation of the max pooling operator. The max pooling operator is a non-linear subsampling function that returns the maximum of a set of values (LeCun et al., 1998). The operator is generalised in two respects. First, k-max pooling over a linear sequence of values returns the subsequence of k maximum values in the sequence, instead of the single maximum value. Secondly, the pooling parameter k can be dynamically chosen by making k a function of other aspects of the network or the input

Am I correct thinking, that this dynamic pooling is for trim sentences(of variable lengths of words), to have vector(vector for supervised learning - see picture on page 4) of constant size?