A very large text file is provided as a list of text chunks in reading order to simulate streaming file reads. Write a function `solution(chunks, k)` that returns the top-`k` most frequent words in the file without concatenating the entire file into one giant string. Normalize tokens using these rules: 1. Apply Unicode normalization with NFKC. 2. Compare words case-insensitively using Unicode `casefold()`. 3. A word consists of Unicode letters and digits, and may contain apostrophes only when the apostrophe is inside the word (for example, `don't` stays one word, but `'hello'` becomes `hello`, and `believin'` becomes `believin`). Treat common Unicode apostrophes like `’` as `'`. 4. Any other character is a separator. 5. Words may be split across chunk boundaries, so your parser must preserve partial tokens between chunks. Return the result as a list of `(word, count)` tuples sorted by descending frequency, then ascending lexicographical word order for ties. If `k` is 0 or there are no words, return an empty list. For the coding portion, implement the exact in-memory approach using a hash map for counts and an efficient top-`k` extraction strategy. In discussion, candidates should be able to compare this approach with tries and external sorting when the distinct-word set barely fits in memory.