Intelligently summarizing and presenting textual responses with machine learning

Abstract

This disclosure relates to methods, non-transitory computer readable media, and systems apply machine-learning techniques and computational sentiment analysis to summarize sentences extracted from a group of textual responses or to select representative-textual responses from the group of textual responses. By using a response-extraction-neural network to extract (and sometimes paraphrase) sentences from textual responses, the disclosed methods, non-transitory computer readable media, and systems can generate a response summary of textual responses based on sentiment indicators corresponding to the textual responses. By applying a machine-learning classifier to generate textual quality scores for textual responses, the disclosed methods, non-transitory computer readable media, and systems select representative-textual responses from a group of textual responses based on relevancy parameters and sentiment indicators corresponding to the textual responses. Such computational techniques generate response summaries and representative responses that provide an efficient snapshot of a group of textual responses analyzed by machine learners.