ให้คะแนนการสร้างข้อความด้วย ROUGE และ BLEU

Summaries and translations are free-form text, so there is no single right answer. We need metrics to compare output against reference text. 📏

Both ROUGE and BLEU compare your generated text to one or more human references. More overlap with the reference means a higher score.

BLEU measures how many short word sequences in your output also appear in the reference. It is the classic metric for machine translation.

BLEU is precision-focused: it asks how much of your output matches the reference. A brevity penalty stops models from cheating with very short text.

ROUGE is the go-to metric for summarization. It checks how much of the reference content your summary managed to recover.

ROUGE leans on recall: did your summary capture the important words from the reference? ROUGE-1 counts single-word overlap.

ROUGE-L looks at the longest matching word sequence, rewarding output that keeps the reference's order, not just its words.

The evaluate library makes scoring a one-liner. Load rouge and pass your predictions with their references.

BLEU works the same way. Note each prediction needs a list of references, since several translations can be correct.

Both scores rise with overlap. They are useful for comparing models, but a single absolute number means little on its own.

These metrics count word overlap, not true meaning. A perfect paraphrase using different words can still score low, so pair them with human review.

Which metric is recall-focused and standard for summarization?

You learned to score generated text: BLEU for translation precision, ROUGE for summary recall, and why both still need human judgment. 🎯