Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task.
The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph.
Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS).
Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.
A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion.