Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: While machine learning models achieve human-comparable performance on sequential data, exploiting structured knowledge is still a challenging problem. Spatio-temporal graphs have been proved to be a useful tool to abstract interaction graphs and previous works exploits carefully designed feed-forward architecture to preserve such structure. We argue to scale such network design to real-world problem, a model needs to automatically learn a meaningful representation of the possible relations. Learning such interaction structure is not trivial: on the one hand, a model has to discover the hidden relations between different problem factors in an unsupervised way; on the other hand, the mined relations have to be interpretable.

In this paper, we propose an attention module able to project a graph sub-structure in a fixed size embedding, preserving the influence that the neighbours exert on a given vertex. On a comprehensive evaluation done on real-world as well as toy task, we found our model competitive against strong baselines.
Student:
A graph neural network able to automatically learn and leverage a dynamic interactive graph structure