Many use cases for GenAI applications follow a summarization pattern, where the input is a document and the output is a summary. These patterns are natively supported with Flexible Evaluation runs. In this guide, we walk the creation of summarization evaluations step by step.

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Application Setup

Evaluations are tied to an external application variant. You will first need to initialize an external application before you can evaluate your application. To create the variant, navigate to the “Applications” page on the SGP dashboard, click Create a new Application, and select External AI as the application template.
You can find the application_variant_id in the top right:

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Summarization Evaluation using the UI

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Create Evaluation Dataset

First, we need to set up an a summarization evaluation dataset. To do this, we can navigate to the “Evaluation Datasets” page in the left hand navigation and hit “Create Dataset” in the top left, chosing “Manual Uplaod”.
In the following modal, select Dataset Type SUMMARIZATION and follow the formatting instructions. Supported file types include CSV, XSLX, JSON and JSONL.

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Upload Outputs

After creating the dataset, you can now upload a set of outputs for your external AI variant using this dataset. Navigate to the application variant you created previously and hit “Upload Outputs” in the top right hand corner.
In the modal, select SUMMARIZATION as the Dataset Type and pick a dataset that matches that schema. If the dataset has multiple version, you will have to select the version of the dataset for which you want to upload the outputs. Ensure to follow the upload instructions for the file type you are choosing. We support the same file types as for the evaluation dataset upload, CSV, XSLX, JSON and JSONL.

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Run Evaluation

After uploading outputs, you can create a new evaluation run. You will need to select an application variant and dataset, including the set of outputs you just uploaded within the given dataset. Furthermore, you will need to select a question set. Note that currently summarization evaluations only support Contributor evlauations and no auto-evaluation from the UI.

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Summarization Evaluation using the SDK

This part of the guide walks through the steps to create and execute an summarization evaluation via our Python SDK.

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Initialize the SGP client

Follow the instructions in the Quickstart Guide to setup the SGP Client. After installing the client, you can import and initialize the client as follows: 
from scale_gp import SGPClient

client = SGPClient(base_url="https://api.egp.scale.com")

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Define and upload summarization test cases

The next step is to create an evaluation dataset for the summarization use case. The SummarizationTestCaseSchema function is a helper function that allows you to quickly create a Summarization Evaluation through the flexible evaluations framework. This function assumes the application you want to evaluate has a document as an input and the expected summary of the document as the expected input. It takes in a document and a expected_summary and creates a test case object. In order to use this function, you start by creating a list of data for your test cases. The test case data is represented as an object that contains a document (a string containing the text of a document you want to summarize) and expected_summary (the expected summarization of this document) key. 
document_data = [
    {
        "document": "The Industrial Revolution, which took place from the 18th to 19th centuries, was a period ... technological advancements of this period laid the groundwork for future innovations and economic growth.",
        "expected_summary": "The Industrial Revolution was a transformative period from the 18th to 19th centuries, marked ..."
    },
    {
        "document": "Quantum computing is an area of computing focused on developing computer technology ... significant investment and research continue in this potentially revolutionary technology.",
        "expected_summary": "Quantum computing is an emerging field that uses quantum mechanics principles to process ..."
    },
    {
        "document": "Climate change refers to long-term shifts in global weather patterns and average temperatures ... addressing climate change will require sustained effort and collaboration at all levels of society.",
        "expected_summary": "Climate change is a global phenomenon primarily driven by human activities, especially the ..."
    }
]
Then, we iterate through this list and create a new list of test case objects with the data that we defined using the SummarizationTestCaseSchema function. 
test_cases = []
for data in document_data:
    tc = SummarizationTestCaseSchema(
        document=data["document"],
        expected_summary=data["expected_summary"]
    )
    test_cases.append(tc)
    print(tc)

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Create the summarization dataset

Next, we create the actual evaluation dataset and upload it to the relevant account in SGP. After running this, you’ll be able to see the evaluation dataset in the UI when navigating to the “Evaluation Datasets” section in the left hand side bar. 
from datetime import datetime
from uuid import uuid4

def timestamp():
    return f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} {uuid4()}"

dataset = DatasetBuilder(client).initialize(
    account_id="your_account_id",
    name=f"Summarization Dataset {timestamp()}",
    test_cases=test_cases
)
print(dataset)

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Configure and run summarization application

def my_summarization_app(prompt, test_case):
    print(prompt['document'][:50])
    start = datetime.now().replace(microsecond=5000)
    return ExternalApplicationOutputFlexible(
        generation_output={
            "generated_summary": "GENERATED OUTPUT SUMMARY"
        },
        trace_spans=[
            {
                "node_id": "formatting",
                "start_timestamp": str(start.isoformat()),
                "operation_input": {
                    "document": "EXAMPLE INPUT DOCUMENT"
                },
                "operation_output": {
                    "formatted_document": "EXAMPLE OUTPUT DOCUMENT FORMATTED"
                },
                "duration_ms": 1000,
            }
        ],
        metrics={"grammar": 0.5}
    )

app = ExternalApplication(client)
app.initialize(application_variant_id="your_variant_id", application=my_summarization_app)
app.generate_outputs(evaluation_dataset_id=dataset.id, evaluation_dataset_version='1')
Note that alternatively to creating a summarization dataset and running the summarization application, you can also use the batch upload functionality, see details here.

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Create evaluation questions

Next up, we need to define the evaluation questions that we want to evaluate for this given summarization app. In the case below we are creating three questions: accuracy, conciseness and missing information. 
question_requests = [
    {
        "type": "categorical",
        "title": "Summarization Accuracy",
        "prompt": "Is this summary accurate with respect to the expected summary provided?",
        "choices": [{"label": "No", "value": 0}, {"label": "Yes", "value": 1}]
    },
    {
        "type": "categorical",
        "title": "Summarization Conciseness",
        "prompt": "Was the summary concise?",
        "choices": [{"label": "No", "value": 0}, {"label": "Yes", "value": 1}]
    },
    {
        "type": "free_text",
        "title": "Summarization Missing Information",
        "prompt": "List relevant information the summary cut out"
    }
]

question_ids = []
for question in question_requests:
    q = client.questions.create(
        **question
    )
    question_ids.append(q.id)
    print(q)

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Create question set and annotation configuration

Finally, we bundle the previously created questions into a “question set” and set up the annotation configuration. The annotation configuration defines the layout and fields that the annotator of the evaluation will see. Because we are using the predefined “summarization” template, we do not need to configure anything here. For more details on all the configuration options for the annotation config, please refer to the “Full Guide to Flexible Evaluation”. 
q_set = client.question_sets.create(
    name="summarization question set",
    question_ids=question_ids
)
print(q_set)

config = client.evaluation_configs.create(
    question_set_id=q_set.id,
    evaluation_type='human'
)
print(config)

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Run the evaluation

With everything set up, we can now run the evaluation by providing the relevant variant and application ids. 
from scale_gp.lib.types import data_locator
from scale_gp.types import SummarizationAnnotationConfigParam

annotation_config_dict = SummarizationAnnotationConfigParam(
    document_loc=data_locator.test_case_data.input["document"],
    summary_loc=data_locator.test_case_output.output["generated_summary"],
    expected_summary_loc=data_locator.test_case_data.expected_output["expected_summary"]
)

evaluation = client.evaluations.create(
    application_variant_id="your_variant_id",
    application_spec_id="your_spec_id",
    description="Demo Evaluation",
    name="Summarization Evaluation",
    evaluation_config_id=config.id,
    annotation_config=annotation_config_dict,
    evaluation_dataset_id=dataset.id,
    type="builder"
)

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Perform Annotations

Once the evaluation run has been created, human annotators can log into the platform and begin completing the evaluation tasks using the task dashboard. For each task, the annotators will see the layout defined by the summarization template and the questions configured in the question set.

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Review Results

As the annotators complete the tasks, we can review the results of the evaluation by navigating to the respective application and clicking on the previously created evaluation run. The results are split into aggregate results and a tabular detail view with all test cases and their annotations.