How do I perform text summarization using OpenAI’s models?

To perform text summarization using OpenAI’s models, you can leverage their API to send a text prompt and receive a condensed version. Start by selecting a model like gpt-3.5-turbo or gpt-4, which are designed for natural language tasks. You’ll need to structure your API request to include the input text and a clear instruction, such as “Summarize this article in three sentences.” The model processes the input, identifies key points, and generates a concise summary. For example, if you input a 500-word news article, the API might return a three-sentence summary highlighting the main event, context, and outcome. This approach works for both single-document summaries and multi-source aggregation, depending on how you format the input.

To implement this, first set up your OpenAI API access by installing the official Python library (openai) and configuring your API key. Construct a request using the ChatCompletion endpoint, specifying the model, a system message (e.g., “You are a helpful assistant that summarizes text”), and a user message containing the text to summarize. Adjust parameters like max_tokens to limit summary length and temperature (lower values like 0.3 produce more focused results). For instance, a Python script might send a prompt like:

response = openai.ChatCompletion.create(
 model="gpt-3.5-turbo",
 messages=[
 {"role": "system", "content": "Summarize the following text in three sentences."},
 {"role": "user", "content": "Long input text here..."}
 ],
 temperature=0.3,
 max_tokens=150
)

The response will include the summary in response.choices[0].message.content. For longer texts, split the input into chunks under the model’s token limit (e.g., 4,096 tokens for gpt-3.5-turbo) to avoid truncation.

Considerations include cost, accuracy, and input formatting. API usage is billed per token, so summarizing large documents can add up—test with shorter texts first. Verify that the summary retains critical information; you may need to refine prompts (e.g., “Focus on technical details” or “Omit examples”). For technical content, explicitly ask the model to avoid simplification. If the input includes structured data (like bullet points), specify whether to preserve formatting. Finally, handle errors such as rate limits by implementing retry logic. By iterating on prompts and parameters, you can tailor outputs for specific use cases, like executive summaries for reports or abstracts for research papers.