CPC G06F 16/24578 (2019.01) [G06F 16/2246 (2019.01); G06F 16/243 (2019.01); G06F 16/248 (2019.01)] | 25 Claims |
1. A computer-implemented method comprising:
constructing, from a set of natural language text documents, a concept tree, a node of the concept tree representing a subject of a subset of the set of natural language text documents (subset), a level of the concept tree corresponding to a level of specificity of a subject of a node in the level;
scoring, for a node in the concept tree (node) using a trained polarity scoring model, a polarity of the subset represented by the node;
adding a second set of natural language text documents to the subset, the adding resulting in a modified subset of natural language text documents, the modified subset having a polarity score within a predefined neutral polarity score range;
selecting, from the modified subset according to a sentence selection parameter, a bin of sentences, a sentence in the bin of sentences extracted from a selected document in the modified subset;
removing, from the bin of sentences, the removing resulting in a filtered bin of sentences, a sentence having a factuality score below a threshold factuality score; and
generating, from the filtered bin of sentences using a transformer deep learning narration generation model, a new natural language text document corresponding to the filtered bin of sentences.
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