metrics/rouge.py

# Copied from https://github.com/huggingface/datasets/blob/d3c7b9481d427ce41256edaf6773c47570f06f3b/metrics/rouge/rouge.py

import nltk
from rouge_score import rouge_scorer, scoring


def compute_rouge(predictions, references, rouge_types=None, use_aggregator=True, use_stemmer=False):
    if rouge_types is None:
        rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"]

    scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer)
    if use_aggregator:
        aggregator = scoring.BootstrapAggregator()
    else:
        scores = []

    for ref, pred in zip(references, predictions):
        score = scorer.score(ref, pred)
        if use_aggregator:
            aggregator.add_scores(score)
        else:
            scores.append(score)

    if use_aggregator:
        result = aggregator.aggregate()
    else:
        result = {}
        for key in scores[0]:
            result[key] = list(score[key] for score in scores)

    return result


# TODO: Check if it is necessary
# Copied from https://github.com/huggingface/transformers/blob/3977b58437b8ce1ea1da6e31747d888efec2419b/examples/pytorch/summarization/run_summarization.py#L520
def rouge_postprocess_text(text):
    # rougeLSum expects newline after each sentence
    return "\n".join(nltk.sent_tokenize(text))