Amazon Technologies, Inc.

Mingwei Shen

Karen Hovsepian

Srikar Appalaraju

Vijay Patha

Thi Nhat Anh Nguyen

Andrew Shanley

Devices and techniques are generally described for generating per-topic review summarization. In some examples, a plurality of text descriptions of a first item may be received. In at least some examples, a list of categories may be determined. In some examples, at least one machine learned model may generate a first summary of the plurality of text descriptions. In some examples, the first summary may be relevant to a first category of the list of categories. In further examples, the at least one machine learned model may generate a second summary of the plurality of text descriptions. The second summary may be relevant to a second category of the list of categories. In various cases, first output data that includes the first summary and the second summary may be generated.

Per-category feedback summarization

Root cause estimation for a data set corresponding to customer returns of a product may use a probabilistic model to associate customer-entered product return data with probability distributions relating to possible root causes for the returns. A particular application relates to applying a Bayesian network to customer-selected return reason codes and customer-entered return reason comments to estimate a probability distribution for root causes of a plurality of returns and uncertainties relating to the probability distribution estimation. A bag-of-n-grams can be used to enable the Bayesian network to process natural language portions of the customer-entered product return data. The output of the model and other data relating to the root cause estimation can be conveyed to a seller of the returned products via a user interface.

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Shoreline, WA, United States of America

Mingwei Shen