Optum Technology, Inc.

Optum, Inc.

Gaurav Ranjan

Suman Roy

Ayan Sengupta

Vijay Varma Malladi

Tanmoy Chakraborty

William Scott Paka

Siddhartha Asthana

Matthew J Stewart

Abraham Gebru Tesfay

Thomas G Sullivan

Gunjan Balde

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for providing a summarization of a conversation, such as a telephonic conversation. In an embodiment, a method is provided. The method comprises receiving an input data object comprising textual data of a conversation, the textual data comprising sentence-level tokens. The method further comprises classifying some sentence-level tokens as interrogative sentence-level tokens, and identifying subtopic portions of the textual data, each interrogative sentence-level token located within one subtopic portion. The method further comprises determining whether an interrogative sentence-level token is substantially similar to one of a plurality of target queries, and for such interrogative sentence-level tokens, selecting sentence-level tokens from a subtopic portion corresponding to the such interrogative sentence-level tokens. The method then comprises generating a summarization data object comprising the selected sentence-level tokens for each interrogative sentence-level token substantially similar to a target query and performing summarization-based actions.

Query-focused extractive text summarization of textual data

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a hybrid reason code prediction machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform natural language processing using a hybrid reason code prediction machine learning framework that comprises one or more of the following: (i) a hierarchical transformer machine learning model, (ii) an utterance prediction machine learning model, (iii) an attention distribution generation machine learning model, (iv) an utterance-code pair prediction machine learning model, and (v) a hybrid prediction machine learning model.

Natural language processing techniques using hybrid reason code prediction machine learning frameworks

There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP-based document prioritization by utilizing joint sentiment-topic (JST) modeling. In one example, a method comprises identifying a JST latent distribution of the digital document that describes topic designation probabilities and sentiment designation probabilities for the digital document; determining, by processing the topic designation probabilities, a document-topic entropy measure for the digital document; determining, by processing the sentiment designation probabilities, a sentiment-topic entropy measure for the digital document; determining, by processing per-word inverse domain frequency measures for the digital, a document popularity measure for the digital document; generating the predicted document priority score based on the document-topic entropy measure, the sentiment-topic entropy measure, and the document popularity measure; and performing one or more prediction-based actions based on the predicted document priority score.

Natural language processing techniques using joint sentiment-topic modeling

There is a need for more effective and efficient predictive natural language summarization. This need can be addressed by, for example, solutions for performing predictive natural language summarization using a constrained optimization model. In one example, a method includes identifying one or more per-party utterance subsets in a multi-party call transcript; generating a plurality of eligible extractive summaries that comply with one or more optimization constraints; for each eligible extractive summary of the plurality of eligible extractive summaries, determining an overall summary utility measure; generating the optimal extractive summary based at least in part on each overall summary utility measure for an eligible extractive summary of the plurality of eligible extractive summaries; and performing one or more summary-based actions based at least in part on the optimal extractive summary.

Natural language processing for optimized extractive summarization

There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP analysis by utilizing joint topic-sentiment (JST) modeling. In one example, a method comprises receiving a per-document topic distribution for a digital document, wherein the per-document topic distribution comprises a per-document topic correlation indication for each candidate topic designation; receiving a per-document topic-sentiment distribution for the digital document, wherein the per-document topic-sentiment distribution comprises a per-document topic-sentiment correlation indication for each topic-sentiment pair of a candidate topic designation and a candidate sentiment designation; generating, based at least in part on the per-document topic distribution and the per-document topic-sentiment distribution, a topic designation and a sentiment designation for each selected word in the digital document; and generating a JST modeling output based a on each topic designation and each sentiment designation.

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Bengaluru, India

Gaurav Ranjan