import re


def remove_company_names(html_contents, name):
    """
    All policies reference their own company/organization names and
    specific service names or collaborator names that only that
    particular organization mentiions in their policy.  If these
    names are referenced often enough, they can skew similarity
    scores.  TfidfVectorizer attempts to balance this out by
    comparing word frequency with document frequency, but this
    is still a good effort to expand upon.

    In:     string representation of the extracted html contents
    Out:    string representation without specific org names
    """
    html_contents = re.sub(name, " ", html_contents, flags=re.IGNORECASE)
    return html_contents