Abstract: The present teaching relates to extracting insightful phrases from an input text. Independent contexts are first identified from the input text. With respect to each independent context, initial candidate phrases are generated with respect to linguistic features and are then filtered. Various features are then computed for each filtered candidate phrase and used to select top k candidate phrases for each independent context. A most insightful phrase is then selected from the k top candidate phrases using deep learned models. Such selected most insightful phrases for the independent contexts are then used for facilitating an understanding the input text.

Abstract: A device may identify, in multi-context text data, unrelated text and coreference text, and may extract coreference clusters, coreference sentences, and coreference sentiments based on the coreference text. The device may extract unrelated sentences from the unrelated text, and may assign tenses to the coreference sentences and the unrelated sentences. The device may extract phrases and entities from the coreference sentences and unrelated sentences, and may assign tense flags that exclude present tense sentences. The device may select past tense phrases and future tense phrases, and may combine the past tense phrases and the future tense phrases to generate phrases. The device may identify invalid phrases in the phrases, and may identify similarities between the coreference sentences and the invalid phrases. The device may process the coreference text, the coreference tenses, the coreference sentiments, and the similarities, with a reinforcement learning model, to generate final context text.

Abstract: A device may receive dynamic customer data and static customer data, and may calculate additional customer data based on the dynamic customer data and the static customer data. The device may process the static customer data, the dynamic customer data, and the additional customer data, with a first machine learning model, to determine a next action prediction, and may process the static customer data, the dynamic customer data, and the additional customer data, with a second machine learning model, to determine a next sequence prediction. The device may concatenate the static customer data, the dynamic customer data, the additional customer data, the next action prediction, and the next sequence prediction to generate concatenated data, and may process the concatenated data, with a plurality of machine learning models, to calculate various outputs, and may generate a recommendation for the customer based on the various outputs.

Abstract: A device may generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.