Task: Summarization
Input (English): The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures.
 In this paper, we propose a pooling operation for GNNs that leverages a differentiable unsupervised loss based on the minCut optimization objective.
 For each node, our method learns a soft cluster assignment vector that depends on the node features, the target inference task (e.g., a graph classification loss), and, thanks to the minCut objective, also on the connectivity structure of the graph.
 Graph pooling is obtained by applying the matrix of assignment vectors to the adjacency matrix and the node features.
 We validate the effectiveness of the proposed pooling method on a variety of supervised and unsupervised tasks.
Output (English): A new pooling layer for GNNs that learns how to pool nodes, according to their features, the graph connectivity, and the dowstream task objective.