Natural language generation using knowledge graph incorporating textual summaries

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions, which, when executed by the system, cause the system to perform a set of actions including:
• receiving a corpus of textual information;
• obtaining textual passages and descriptions of associations between textual passages from a first language model by providing at least a portion of the corpus of textual information to the first language model, wherein the first language model is pre-trained to produce an output of textual passages and descriptions of associations between textual passages based on an input to the first language model without reference to a pre-determined ontology;
• obtaining textual summaries corresponding to textual passages from a second language model;
• generating a knowledge graph based on the textual passages, descriptions of associations between the textual passages, and textual summaries;
• storing the knowledge graph in a non-transitory computer readable medium;
• receiving a natural language textual sequence representing a textual input;
• retrieving a set of textual passages from the knowledge graph by comparing the natural language textual sequence against the textual passages in the knowledge graph using a cosine similarity metric, wherein the set of textual passages are retrieved by ranking the textual passages based on the comparison, and selecting the set of textual passages having a value over a threshold value;
• and
• obtaining a textual output to the textual input from the second language model by providing the second language model with the natural language textual sequence, the set of textual passages, and the textual summaries.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform part or all of one or more methods disclosed herein:

• receiving a corpus of textual information;
• obtaining textual passages and descriptions of associations between textual passages from a first language model by providing at least a portion of the corpus of textual information to the first language model, wherein the first language model is pre-trained to produce an output of textual passages and descriptions of associations between textual passages based on an input to the first language model without reference to a pre-determined ontology;
• obtaining textual summaries corresponding to textual passages from a second language model;
• generating a knowledge graph based on the textual passages, descriptions of associations between the textual passages, and textual summaries;
• storing the knowledge graph in a non-transitory computer readable medium;
• receiving a natural language textual sequence representing a textual input;
• retrieving a set of textual passages from the knowledge graph by comparing the natural language textual sequence against the textual passages in the knowledge graph using a cosine similarity metric, wherein the set of textual passages are retrieved by ranking the textual passages based on the comparison, and selecting the set of textual passages having a value over a threshold value;
• and
• obtaining a textual output to the textual input from the second language model by providing the second language model with the natural language textual sequence, the set of textual passages, and the textual summaries.