In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to
understand information that cannot be inferred when considering sentences in isolation. First, we design a graph structure in which each
node represents an individual sentence, and some pairs of nodes are selectively connected based on the text structure. Then, we develop
an iterative attentive aggregation and a skip-combine method in which a node interacts with its neighborhood nodes to accumulate the
necessary information. To evaluate the performance of the proposed approaches, we conduct experiments with the standard HotpotQA
dataset. The empirical results demonstrate the superiority of our proposed approach, which obtains the best performances, compared to
the widely used answer-selection models that do not consider the intersentential relationship.