The Agent Assist Summarizationfeature lets you provide conversation summaries to your agents after each conversation is completed. The summaries help agents create their conversation notes and understand end-user communication history. For example, a summary output about a conversation might look similar to the following:
This tutorial guides you through training and deploying a Summarization model using the Agent Assist console. You can use it to train a model and test its performance, but be aware that all runtime operations must be carried out by calling the API directly. See the Agent Assist Summarization how-to guide for instructions.
If preferred, you can also create and deploy a Summarization model by calling the API directly
Before you begin
- If you are using your own data, make sure that you have formatted it correctly and uploaded it to a Cloud Storage bucket. You also have the option of training a model using demo chat data or using a pre-trained demo model.
Create & train a new model
Navigate to the Agent Assist console . Select the Summarization card in the center of the screen and click Get started. You have the option of trying out the Summarization feature using a demo model, or creating your own custom model using one or more datasets.
If you are training a custom model using the public Summarization dataset,
enter gs://summarization_integration_test_data/data/*
in the dataset URI
field. If you are using your own dataset, the tutorial will walk you through
the process of creating a dataset from your data.
What's next
After you have deployed your model, you can then proceed to create a conversation profile .
