Systems for Generating Indications of Relationships between Electronic Documents

One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a processing device, causes the processing device to perform operations including:

• forming positive training sets of electronic documents to train a fully convolutional network that each include a first version of an electronic document and a second version of the electronic document;
• generating a first heatmap for each of the positive training sets by modeling lexical similarity between sentences included in the first version of the electronic document and sentences included in the second version of the electronic document;
• generating a second heatmap for each of the positive training sets by modeling a similarity between entities of the sentences included in the first version of the electronic document and entities of the sentences included in the second version of the electronic document;
• compressing the first heatmaps and the second heatmaps into feature vectors using an encoder of the fully convolutional network;
• training the fully convolutional network to classify pairs of electronic documents received as input using the feature vectors and a loss function;
• and
• generating indications of relationships between particular electronic documents based at least partially on electronic document pairs that are classified by the fully convolutional network as semantically similar documents.

A system comprising:

• a processing device;
• and
• computer-readable storage media storing instructions that are executable by the processing system to perform operations including:
• forming positive training sets of electronic documents to train a fully convolutional network that each include a first version of an electronic document and a second version of the electronic document;
• generating a first heatmap for each of the positive training sets by modeling lexical similarity between sentences included in the first version of the electronic document and sentences included in the second version of the electronic document;
• generating a second heatmap for each of the positive training sets by modeling a similarity between entities of the sentences included in the first version of the electronic document and entities of the sentences included in the second version of the electronic document;
• compressing the first heatmaps and the second heatmaps into feature vectors using an encoder of the fully convolutional network;
• training the fully convolutional network to classify pairs of electronic documents using the feature vectors and a loss function;
• classifying, using the fully convolutional network, electronic document pairs received as input to the fully convolutional network as semantically similar documents or not semantically similar documents to generate an indication of a classification for the electronic document pairs as an output;
• and
• generating indications of relationships between the electronic document pairs based at least partially on the electronic document pairs that are classified as semantically similar documents.