To use OpenAI for summarizing long documents, you can leverage the API with a combination of text chunking, prompt engineering, and iterative processing. Start by splitting your document into manageable segments that fit within OpenAI’s token limits (e.g., 4,096 tokens for gpt-3.5-turbo). Use simple text-splitting logic, like dividing by paragraphs or sentences, or employ libraries like tiktoken to count tokens precisely. Send each chunk to the API with a clear prompt, such as “Summarize the following text in 3-4 sentences,” and collect the results. For very long documents, you may need to summarize chunks recursively—summarize sections first, then combine those summaries into a final summary.
Here’s a practical example using Python. Install the openai library and set up your API key. Load your document, split it into chunks, and loop through each segment:
import openai
openai.api_key = "YOUR_KEY"
text_chunks = [chunk1, chunk2, ...] # Your preprocessed chunks
summaries = []
for chunk in text_chunks:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": f"Summarize this in 3 sentences: {chunk}"}]
)
summaries.append(response.choices[0].message.content)
final_summary = "\n".join(summaries)
If the combined summaries are still too long, repeat the process on the final_summary text. Adjust the prompt or model parameters (like temperature=0.3 for more focused output) to improve clarity.
For best results, preprocess the text to remove irrelevant content (e.g., headers, footers) and test different prompts. For instance, “Identify key points in 2 bullet points” might yield better structure than a generic request. Monitor API costs and performance—summarizing large documents can require multiple calls. If you need higher token limits, consider using gpt-4 (8k or 32k token contexts), but weigh the cost trade-offs. Always validate outputs for accuracy, as the model might occasionally omit critical details or misinterpret complex sections.