How to Summarize PDF Text with Python

Summarize PDF Content in a Python Application

Long reports, research papers, contracts, and manuals can contain far more information than a user needs for an initial review. The pdfRest Summarize PDF API Tool condenses PDF, Markdown, or plain-text content into a summary that a Python application can display, store, or pass into a larger workflow. The tool handles document text extraction, preserves useful structure as Markdown, and performs the summarization behind one API endpoint, so you do not have to assemble and maintain separate PDF parsing and AI integrations.

The /summarized-pdf-text endpoint accepts either an uploaded file or the resource ID of a file already on the pdfRest processing service. Uploading a file is the simplest way to try the endpoint. Resource IDs are useful when summarization follows another pdfRest operation because the application can chain the calls without downloading and uploading the intermediate file.

Python Summarize PDF Code Example

The multipart example uses requests to send the request and requests-toolbelt to encode the PDF and parameters. Install both packages, replace the file path and API-key placeholders, and run the script from an environment that can reach the pdfRest Cloud API.

from requests_toolbelt import MultipartEncoder
import requests
import json

# By default, we use the US-based API service. This is the primary endpoint for global use.
api_url = "https://api.pdfrest.com"

# For GDPR compliance and enhanced performance for European users, you can switch to the EU-based service by uncommenting the URL below.
# For more information visit https://pdfrest.com/pricing#how-do-eu-gdpr-api-calls-work
#api_url = "https://eu-api.pdfrest.com"

endpoint_url = api_url+'/summarized-pdf-text'

# The endpoint can take a single PDF file or id as input.
mp_encoder = MultipartEncoder(
    fields={
        'file': ('file_name.pdf', open('/path/to/file', 'rb'), 'application/pdf'),
        'target_word_count': '100',
    }
)

headers = {
    'Accept': 'application/json',
    'Content-Type': mp_encoder.content_type,
    'Api-Key': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'
}

print("Sending POST request to summarized-pdf-text endpoint...")
response = requests.post(endpoint_url, data=mp_encoder, headers=headers)

print("Response status code: " + str(response.status_code))

if response.ok:
    response_json = response.json()
    print(json.dumps(response_json, indent=2))
else:
    print(response.text)

Source: pdfRest Summarize PDF multipart Python sample on GitHub

If the source file already has a pdfRest resource ID, send a JSON payload instead of uploading it again as multipart data. See the Summarize PDF JSON-payload sample.

Control the Length and Structure of the Summary

The sample sets target_word_count to 100. This value is a target rather than an exact limit; the generated result may be somewhat shorter or longer when that produces a more coherent summary. If the parameter is omitted, the current default is 400 words.

Additional parameters let you tailor the result to the application instead of accepting one generic block of summary text:

• Use summary_format to request an overview, highlights, an abstract, bullet points, a numbered list, a table of contents, an outline, questions and answers, or action items.
• Use pages to summarize only the relevant page ranges, such as the body of a report without its cover pages or appendices.
• Use output_format to choose structured Markdown or plain text.
• Use output_type to receive the summary in the JSON response or as a downloadable output file.

For example, an internal research portal might request an abstract, while a meeting-document workflow could request action items. A knowledge-base application may prefer Markdown because headings and lists are ready to render. These controls make the same endpoint useful across several products and workflows without requiring prompt engineering or custom formatting logic for each one.

Handle the API Response in Python

The sample checks response.ok before parsing JSON. In production, also set an appropriate timeout, catch network exceptions, and log the HTTP status and a safe error message when a request fails. Avoid printing API keys, source documents, or sensitive summary content into shared logs.

Inspect the response structure before reading a summary field, especially if your application allows callers to change output_type. When a file is returned, store the output ID or download URL according to the application's retention and access-control requirements. When summary text is returned inline, validate that the expected field is present before displaying or saving it.

Present Summaries with Source Context

Summaries accelerate review by giving readers a focused orientation to longer material. For legal, medical, financial, or other high-stakes uses, an application can display the summary beside a link to the source PDF, making exact wording and supporting context immediately available when needed.

Summarize PDF converts the document content to Markdown before summarization so headings, lists, and other structure can provide richer context. Your submitted files and data are never used to train AI models. Current summarization parameters and response schemas are documented in the Summarize PDF API reference, and you can generate requests in API Lab before adding the call to your application.