Salesforce, Inc.

Victor Yee

Zachary Alexander

Na Cheng

Sitaram Asur

Caiming Xiong

Anuprit Kale

Shashank Harinath

Chien-Sheng Wu

Wojciech Kryscinski

Alexander R Fabbri

Feifei Jiang

Aron Kale

Jacob Nathaniel Huffman

Sameer Abhinkar

Nicholas Feinig

Fermin Ordaz

Embodiments described herein provide a structured conversation summarization framework. A user interface may be provided which allows an agent to perform a conversation with a customer, for example regarding resolving a customer support issue. Utterances by both the agent and customer may be stored, and at the end of the conversation, the utterances may be used to generate a structured summary. The structured summary may include components such as a general summary, an issue summary, and a resolution summary. Using neural network models and heuristics, each component of the summary may be automatically generated.

Systems and methods for structured conversation summarization

Embodiments described herein provide systems and methods for document recommendation. A system receives a set of training data including a plurality of documents. The system determines whether the set of training data includes annotated contextual information corresponding to the plurality of documents. The system trains supervised and/or unsupervised models based on the availability of data. The models are used to generate vectors representing the documents. During a live text conversation, text from the conversation may be vectorized using the models and the vectors compared to those representing the documents in order to find the most relevant documents. The system may generate an indication of a recommended document.

Systems and methods for real-time document recommendation

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Oakland, CA, United States of America

Victor Yee