Abstract: Various embodiments provide for summarization of an interaction, conversation, encounter, and/or the like in at least an abstractive manner. In one example embodiment, a method is provided. The method includes generating, using an encoder-decoder machine learning model, a party-agnostic representation data object for each utterance data object. The method further includes generating an attention graph data object to represent semantic and party-wise relationships between a plurality of utterance data objects. The method further includes modifying, using the attention graph data object, the party-agnostic representation data object for each utterance data object to form a party-wise representation data object for each utterance data object. The method further includes selecting a subset of party-wise representation data objects for each of a plurality of parties.

Abstract: 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.

Abstract: Various embodiments of the present disclosure provide summarization techniques for summarizing complex documents, such as long unstructured call transcripts. The summarization techniques include generating a plurality of interaction topics for an interaction transcript and iteratively summarizing each interaction topic based on a preceding partial summary for the interaction transcript that corresponds to a preceding interaction topic that precedes the interaction topic in the interaction transcript. An abstractive summary is generated using a recursive abstractive model that is trained using training data generated based on holistic similarity scores between interaction topics of a call transcript and summary sentences of a corresponding target summary.

Abstract: Various embodiments of the present disclosure provide summarization techniques for summarizing complex documents, such as long unstructured call transcripts. The summarization techniques include generating a plurality of interaction topics for an interaction transcript and iteratively summarizing each interaction topic based on a preceding partial summary for the interaction transcript that corresponds to a preceding interaction topic that precedes the interaction topic in the interaction transcript. An abstractive summary is generated using a recursive abstractive model that is trained using training data generated based on holistic similarity scores between interaction topics of a call transcript and summary sentences of a corresponding target summary.

Abstract: There is a need for more effective and efficient text categorization. This need can be addressed by, for example, techniques for semantic text categorization. In one example, a method includes determining an input vector-based representation of an input document; processing the input vector-based representation using a trained supervised machine learning model to generate the categorization based at least in part on the input vector-based representation, wherein: (i) the trained supervised machine learning model has been trained using automatically-generated training data, and (ii) the automatically generated training data is generated by determining an inferred semantic label for each unlabeled training document of one or more unlabeled training documents; and performing one or more categorization-based actions based at least in part on the categorization, and (iii) the labels are described by one or more short documents/short texts.

Abstract: There is a need for more effective and efficient predictive natural language topic detection. This need can be addressed by, for example, solutions for performing sequential topic detection. In one example, a method includes determining a sequential topic distribution data object for the current document sequence, determining a current term-context correlation data object for the current document sequence, determining a current context-topic correlation data object for the current document sequence, determining an updated term-topic correlation data object based at least in part on the current context-topic correlation data object, determining topic modeling predictions based at least in part on the sequential topic distribution data object and the updated term-topic correlation data object, and performing prediction-based actions based at least in part on the topic modeling predictions.

Abstract: Various embodiments provide for summarization of an interaction, conversation, encounter, and/or the like in at least an abstractive manner. In one example embodiment, a method is provided. The method includes generating, using an encoder-decoder machine learning model, a party-agnostic representation data object for each utterance data object. The method further includes generating an attention graph data object to represent semantic and party-wise relationships between a plurality of utterance data objects. The method further includes modifying, using the attention graph data object, the party-agnostic representation data object for each utterance data object to form a party-wise representation data object for each utterance data object. The method further includes selecting a subset of party-wise representation data objects for each of a plurality of parties.

Abstract: 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.

Abstract: As described herein, 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 combination of a cross-token attention machine learning, a cross-utterance attention machine learning model, and an integer linear programming joint keyword-utterance optimization model to select an extractive keyword summarization of a multi-party communication transcript data object that comprises a selected utterance subset of U utterances (e.g., U sentences) of a document data object and a selected keyword subset of K candidate keywords of the document data object.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: There is a need for more effective and efficient predictive natural language topic detection. This need can be addressed by, for example, solutions for performing sequential topic detection. In one example, a method includes determining a sequential topic distribution data object for the current document sequence, determining a current term-context correlation data object for the current document sequence, determining a current context-topic correlation data object for the current document sequence, determining an updated term-topic correlation data object based at least in part on the current context-topic correlation data object, determining topic modeling predictions based at least in part on the sequential topic distribution data object and the updated term-topic correlation data object, and performing prediction-based actions based at least in part on the topic modeling predictions.

Abstract: There is a need for solutions for more effective and efficient natural language processing systems for short texts. This need can be addressed, for example, by a system configured to obtain an initial term-topic correlation data object for a plurality of digital documents, obtain a user-defined term-topic correlation data object for the plurality of digital documents, generate a refined term-topic correlation data object and a refined document-sentiment correlation data object for the plurality of digital documents based at least in part on the initial term-topic correlation data object and the user-defined term-topic correlation data object, obtain a user-defined document-topic correlation data object for the plurality of digital documents, and generate a refined document-topic correlation object for the plurality of digital documents based at least in part on the refined term-topic correlation data object and the user-defined document-topic correlation data object.

Abstract: There is a need for more effective and efficient text categorization. This need can be addressed by, for example, techniques for semantic text categorization. In one example, a method includes determining an input vector-based representation of an input document; processing the input vector-based representation using a trained supervised machine learning model to generate the categorization based at least in part on the input vector-based representation, wherein: (i) the trained supervised machine learning model has been trained using automatically-generated training data, and (ii) the automatically generated training data is generated by determining an inferred semantic label for each unlabeled training document of one or more unlabeled training documents; and performing one or more categorization-based actions based at least in part on the categorization, and (iii) the labels are described by one or more short documents/short texts.

Abstract: There is a need for solutions for more effective and efficient natural language processing systems for short texts. This need can be addressed, for example, by a system configured to obtain an initial term-topic correlation data object for a plurality of digital documents, obtain a user-defined term-topic correlation data object for the plurality of digital documents, generate a refined term-topic correlation data object and a refined document-sentiment correlation data object for the plurality of digital documents based at least in part on the initial term-topic correlation data object and the user-defined term-topic correlation data object, obtain a user-defined document-topic correlation data object for the plurality of digital documents, and generate a refined document-topic correlation object for the plurality of digital documents based at least in part on the refined term-topic correlation data object and the user-defined document-topic correlation data object.

Abstract: A Secure Virtual Point of Service (SVPOS) that coordinates the authentication, authorization, and identity, settlement, arbitration and non-repudiation for an electronic commercial transaction. For each commercial transaction, both the buyer and merchant authenticate itself to the SVPOS and create two unique transaction encryption keys, one for the buyer and one for the merchant. The merchant uses both encryption keys to encrypt a package that include at least product identification. The merchant and buyer calculate a hash of the package and transmit the calculated hash to the SVPOS for comparison to prevent repudiation. If the calculated hash is identical the buyer receives the merchants encryption key and decrypts the package. Payment is released by the SVPOS if the buyer is satisfied with the package via a Parlay system. If the buyer is not satisfied, said SVPOS performs arbitration between the buyer and merchant to determine if the package is correct.