def acs(concept, paragraph, model, min_length, stop_words=[]):
    """
    :param concept: str, the concept word for the concept
    :param paragraph: list, a list of the tokens in the paragraph
    :param model: gensim.models.Word2Vec, model containing word vectors
    :return: float, the distance between the concept and the paragraph
            as the sum of the cos distance between each word and the concept
            divided by the number of words.
    """
    d_sum = 0
    word_count = 0
    if len(paragraph) == 0:
        return d_sum
    if len(paragraph) <= min_length:
        return -1
    for word in paragraph:
        if word in model.wv and word not in stop_words:
            sim = model.wv.similarity(word, concept)
            d_sum += sim
            word_count += 1

    return d_sum/word_count