sample_abstract_summary.py

# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------

"""
FILE: sample_abstract_summary.py

DESCRIPTION:
    This sample demonstrates how to submit text documents for abstractive text summarization.
    Abstractive summarization is available as an action type through the begin_analyze_actions API.

    Abstractive summarization generates a summary that may not use the same words as those in
    the document, but captures the main idea.

USAGE:
    python sample_abstract_summary.py

    Set the environment variables with your own values before running the sample:
    1) AZURE_LANGUAGE_ENDPOINT - the endpoint to your Language resource.
    2) AZURE_LANGUAGE_KEY - your Language subscription key
"""


def sample_abstractive_summarization() -> None:
    # [START abstract_summary]
    import os
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.textanalytics import TextAnalyticsClient

    endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
    key = os.environ["AZURE_LANGUAGE_KEY"]

    text_analytics_client = TextAnalyticsClient(
        endpoint=endpoint,
        credential=AzureKeyCredential(key),
    )

    document = [
        "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, "
        "human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive "
        "Services, I have been working with a team of amazing scientists and engineers to turn this quest into a "
        "reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of "
        "human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the "
        "intersection of all three, there's magic-what we call XYZ-code as illustrated in Figure 1-a joint "
        "representation to create more powerful AI that can speak, hear, see, and understand humans better. "
        "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, "
        "spanning modalities and languages. The goal is to have pretrained models that can jointly learn "
        "representations to support a broad range of downstream AI tasks, much in the way humans do today. "
        "Over the past five years, we have achieved human performance on benchmarks in conversational speech "
        "recognition, machine translation, conversational question answering, machine reading comprehension, "
        "and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious "
        "aspiration to produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
        "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational "
        "component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
    ]

    poller = text_analytics_client.begin_abstract_summary(document)
    abstract_summary_results = poller.result()
    for result in abstract_summary_results:
        if result.kind == "AbstractiveSummarization":
            print("Summaries abstracted:")
            [print(f"{summary.text}\n") for summary in result.summaries]
        elif result.is_error is True:
            print("...Is an error with code '{}' and message '{}'".format(
                result.error.code, result.error.message
            ))
    # [END abstract_summary]


if __name__ == "__main__":
    sample_abstractive_summarization()