code: def get_addition_score(source_counts, prediction_counts, target_counts):
    
    added_to_prediction_counts = prediction_counts - source_counts
    true_positives = sum((added_to_prediction_counts & target_counts).values())
    selected = sum(added_to_prediction_counts.values())
    # Note that in the paper the summation is done over all the ngrams in the
    # output rather than the ngrams in the following set difference. Since the
    # former does not make as much sense we compute the latter, which is also done
    # in the GitHub implementation.
    relevant = sum((target_counts - source_counts).values())
    return _get_fbeta_score(true_positives, selected, relevant)
docs: Compute the addition score (Equation 4 in the paper).