BLEU evaluates machine translations by comparing n-grams to reference human translations, while ROUGE evaluates summaries by measuring co-occurrence of n-grams between machine and human summaries. ROUGE measures recall by counting overlapping n-grams between the reference and machine summaries, while BLEU measures precision by counting n-grams in the machine translation that appear in the reference translations. Both are complementary metrics as high BLEU indicates more words from the machine translation are in the references and high ROUGE means more words from the references are in the machine translation.

1. BLEU Evaluation vs
Rouge Evaluation

2. BLEU
the closer a machine translation is to a professional human
translation, the better it is
brevity penalty : defined to be e^(1-r/c)

3. ROUGE
 ROUGE-N: N-gram based co-occurrence statistics.
 ROUGE-L: Longest Common Subsequence (LCS) based statistics. Longest
common subsequence problem takes into account sentence level structure
similarity naturally and identifies longest co-occurring in sequence n-grams
automatically.
 ROUGE-W: Weighted LCS-based statistics that favors consecutive
 ROUGE-S: Skip-bigram based co-occurrence statistics. Skip-bigram is any pair
of words in their sentence order.
 ROUGE-SU: Skip-bigram plus unigram-based co-occurrence statistics.

4.  Bleu measures precision: how much the words (and/or n-grams) in the
machine generated summaries appeared in the human reference summaries.
 Rouge measures recall: how much the words (and/or n-grams) in the
human reference summaries appeared in the machine generated summaries.

5. BLEU

6.  these results are complementing, as is often the case in precision vs recall.
 If you have many words from the system results appearing in the human
references you will have high Bleu
 if you have many words from the human references appearing in the system
results you will have high Rouge.

7. 𝐹1 =
2 ∗ (𝐵𝐿𝐸𝑈 ∗ 𝑅𝑜𝑢𝑔𝑒)
(𝐵𝐿𝐸𝑈 + 𝑅𝑜𝑢𝑔𝑒)