International Business Machines Corporation

Jonathan Herzig

David Konopnicki

Michal Shmueli-Scheuer

Tommy Sandbank

Haggai Roitman

Yosi Mass

Guy Lev

Achiya Jerbi

Embodiments may provide techniques to generate training data for summarization of complex documents, such as scientific papers, articles, etc., that are scalable to provide large scale training data. For example, in an embodiment, a method may be implemented in a computer system and may comprise collecting a plurality of video and audio recordings of presentations of documents, collecting a plurality of documents corresponding to the video and audio recordings, converting the plurality of video and audio recordings of presentations of documents into transcripts of the plurality of presentations, generating a summary of each document by selecting a plurality of sentences from each document using the transcript of the that document, generating a dataset comprising a plurality of the generated summaries, and training a machine learning model using the generated dataset.

Automatic generation of training data for scientific paper summarization using videos

A method, computer system, and a computer program product for optimizing a plurality of personality traits of a virtual agent based on a predicted customer satisfaction value is provided. The present invention may include identifying a customer. The present invention may also include retrieving a plurality of data associated with the customer. The present invention may then include analyzing the received plurality of data using a customer satisfaction prediction model. The present invention may further include generating a plurality of analyzed data from the customer satisfaction prediction model based on the analyzed plurality of data. The present invention may also include generating a plurality of personality traits for a virtual agent from the generated plurality of analyzed data.

Optimizing personality traits of virtual agents

Embodiments of the present systems and methods may provide techniques by which bots may be analyzed using improved representations of bot structure and a means of assessing conversational quality that may provide improved efficiency. For example a method may comprise training, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, a neural network model to learn representations that capture characteristics of the graphs of chatbots, wherein the captured characteristics include at least a content-based representation based on user utterances that are relevant to the nodes and based on the chatbot response for the nodes.

Neural representation of automated conversational agents (chatbots)

Utilizing a computing device to detect and respond to emotion in dialog systems. The computing device receives a dialog structure comprising a plurality of dialog nodes. The computing device determines a node emotion level for each of the dialog nodes in the dialog structure based on analysis of one or more intents of each of the dialog nodes in the dialog structure. The computing device determines emotional hotspot nodes in the dialog structure, the node emotion level for each of the emotional hotspot nodes exceeding an emotional threshold. The computing device generates one or more responses modifying the node emotion level of each of the emotional hotspot nodes.

Emotion detection and expression integration in dialog systems

Identifying influencers in a computer network by adjusting influence weights of corresponding participants of a computer network, where the influence weights relate to a topic, where each of the influence weights is adjusted in accordance with a predefined adjustment function, and where the predefined adjustment function uses a) the influence weight of any of the participants that disseminated content via the computer network, where the content relates to the participant whose influence weight is being adjusted, b) a participant topic similarity value of any of the participants that disseminated the content, where the participant topic similarity value relates to the topic, and c) a relationship topic similarity value of any relationship between the participants that disseminated the content and the participant whose influence weight is being adjusted, where the relationship topic similarity value relates to the topic, and then ranking the participants by their influence weights.

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Tel Aviv, Israel

Jonathan Herzig