def extract_features_basic(label_dict, ind_label_list):
    """
    Takes a dictionary of predicted labels and a list of target word and extracts different scores:
        maxscorevalue_dict: the maximum score of a label contained in the target list
        product_ref_dict: is a product referenced at least once
        product_count_dict: how often is a product referenced?
        product_sum_dict: the sum of all label scores associated with the target list

    """
    maxscorevalue_dict = 0 # highest probability score of relevant label
    product_ref_dict = 0 # is there at least one product reference
    product_count_dict = 0 # number of product references
    product_sum_dict = 0 # number of product references weighted by score

    for label in label_dict.keys():
        words = label.split(" ")
        if list(set(words) & set(ind_label_list)) != []:
            product_ref_dict = 1
            product_count_dict += 1
            product_sum_dict += label_dict[label]
            if label_dict[label] > maxscorevalue_dict:
                maxscorevalue_dict = label_dict[label]

    score_dict = {"max_score":maxscorevalue_dict,
                "product_reference": product_ref_dict,
                "product_ref_count": product_count_dict,
                "product_ref_sum": product_sum_dict}

    return score_dict