nltk word_tokenize: why do sentence tokenization before word tokenization?

As noted in the source code, word_tokenize runs a sentence tokenizer(Punkt) before running the word tokenizer(Treebank):

# Standard word tokenizer.
_treebank_word_tokenizer = TreebankWordTokenizer()

def word_tokenize(text, language='english', preserve_line=False):
    """
    Return a tokenized copy of *text*,
    using NLTK's recommended word tokenizer
    (currently an improved :class:`.TreebankWordTokenizer`
    along with :class:`.PunktSentenceTokenizer`
    for the specified language).
    :param text: text to split into words
    :param text: str
    :param language: the model name in the Punkt corpus
    :type language: str
    :param preserve_line: An option to keep the preserve the sentence and not sentence tokenize it.
    :type preserver_line: bool
    """
    sentences = [text] if preserve_line else sent_tokenize(text, language)
    return [token for sent in sentences
            for token in _treebank_word_tokenizer.tokenize(sent)]

What is the benefit of doing sentence tokenization prior to word tokenization?