Automated document adaptation based on topic classification

A computer program product for document adjustment, the computer program product comprising:

• one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising:
• program instructions to classify, using a logistic regression classification model executing on a processor, a topic into an interaction type in a set of predefined interaction types;
• program instructions to extract, from a document repository, a set of documents corresponding to the topic;
• program instructions to score, using a generative adversarial model executing on a processor, a sentiment corresponding to a reaction to a previous presentation, the scoring resulting in a scored sentiment;
• program instructions to weight, using a trained attention layer model, the interaction type, the set of documents, and the scored sentiment, the weighting generating a weighted interaction type, a weighted set of documents, and a weighted scored sentiment;
• and
• program instructions to adjust, using a natural language generation transformer model executing on the processor according to the weighted interaction type and the weighted scored sentiment, a document in the weighted set of documents.

A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:

• program instructions to classify, using a logistic regression classification model executing on a processor, a topic into an interaction type in a set of predefined interaction types;
• program instructions to extract, from a document repository, a set of documents corresponding to the topic;
• program instructions to score, using a generative adversarial model executing on a processor, a sentiment corresponding to a reaction to a previous presentation, the scoring resulting in a scored sentiment;
• program instructions to weight, using a trained attention layer model, the interaction type, the set of documents, and the scored sentiment, the weighting generating a weighted interaction type, a weighted set of documents, and a weighted scored sentiment;
• and
• program instructions to adjust, using a natural language generation transformer model executing on the processor according to the weighted interaction type and the weighted scored sentiment, a document in the weighted set of documents.