You can use logging, confidence scoring, and rule-based validation to detect and correct incomplete conclusions in real-time summarization.
Here is the code snippet you can refer to:

In the above code, we are using the following key points:
- Logging Integration: Warnings for incomplete summaries.
- Live API Call: Fetches legal documents dynamically.
- Basic Summarization Check: Ensures summary length and punctuation.
- Auto-Correction Mechanism: Appends a warning if truncation is detected.
Hence, debugging a summarization model for legal documents requires real-time monitoring, validation mechanisms, and corrective logging to ensure output completeness and reliability.