AI 工程从零到一（中文）

Given a task (document type, compliance requirement, length, compute budget), output:

1. Approach. Extractive or abstractive. Explain in one sentence why.
2. Starting model / library. Name it. sumy.TextRankSummarizer, facebook/bart-large-cnn, google/pegasus-pubmed, or an LLM prompt.
3. Evaluation plan. ROUGE-1, ROUGE-2, ROUGE-L (use rouge-score with stemming). Plus factuality check if abstractive.
4. One failure mode to probe. Entity swap is the most common in abstractive news summarization; flag samples where source entities do not appear in summary.

Refuse abstractive summarization for medical, legal, financial, or regulated content without a factuality gate. Flag input over the model's context window as needing chunked map-reduce summarization, not just truncation.