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I won't post the code here, but here's how I solved it:

1. Setup (spliting words in spaces and special characters)
2. Create a dictionary counting the ammount of words for each text
3. Create a global dictionary for the total ammount of words
4. Normalize each local dictionary, considering the global frequency
5. Considering each word as a vector, calculate cosine distance between every text from setA versus setB.
6. Create similarity matrix
7. Find the permutation matrix to maximize PxS

Got 100% with this approach

For help, you can follow up the link:

https://github.com/nishant-sethi/HackerRank/

Hmm. With every optimization I can think of and a conflict-resolution scheme between the matches I'm still stuck at 96.67%. Really quite discouraging. Why's the solved score set so high?

Edit: got the 100 with a small tweak, but I'd still suggest adding a little leeway in the score.