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（徽标呼啸而过）
(logo whooshing)

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- 什么是 ROUGE 指标？
- What is the ROUGE metric?

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对于许多 NLP 任务
For many NLP tasks

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我们可以使用常见的指标，如准确性或 F1 分数。
we can use common metrics like accuracy or F1 score.

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但是当你想评估类似从像 T5 这样的模型上
But what do you do when you wanna measure something

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所获得的文本摘要的质量，该如何操作呢？
like the quality of a summary from a model like T5?

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在本视频中，我们将了解
In this video, we'll take a look

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被广泛用于文本摘要的评估指标，称为 ROUGE。
at a widely used metric for text summarization called ROUGE.

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ROUGE 实际上有几种变体
There are actually several variants of ROUGE

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但所有这些变体背后的基本思想
but the basic idea behind all of them

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是为每个摘要分配一个单独的分数
is to assign a single numerical score

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来告诉我们相比一个或者多个参考的摘要
to a summary that tells us how good it is

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当前的摘要有多好。
compared to one or more reference summaries.

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在这个例子中，我们有一个书评
In this example, we have a book review

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它是通过某些模型摘要获得。
that has been summarized by some model.

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如果我们将生成的摘要
If we compare the generated summary

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和一些人工的摘要相比较，
to some reference human summaries, 

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我们可以看到该模型实际上非常好
we can see that the model is actually pretty good

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并且只相差一两个词。
and only differs by a word or two.

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那么我们如何通过自动的方式
So how can we measure the quality

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评估生成的摘要的质量呢？
of a generated summary in an automatic way?

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ROUGE 采用的方法是比较生成的摘要的 n-gram
The approach that ROUGE takes is to compare the n-grams

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和参考文献的 n-gram。
of the generated summary to the n-grams of the references.

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n-gram 只是一种表达 N 个单词的词块的流行说法。
And n-gram is just a fancy way of saying a chunk of N words.

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所以让我们从 unigram 开始
So let's start with unigrams

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它对应于句子中的各个单词。
which correspond to the individual words in a sentence.

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在这个例子中，你可以看到
In this example, you can see that six

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在生成的摘要中有 6 个单词，也出现在
of the words in the generated summary are also found

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其中一个参考的摘要中。
in one of the reference summaries.

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而比较 unigram 的 rouge metric
And the rouge metric that compares unigrams

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被称为 ROUGE-1。
is called ROUGE-1.

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现在我们找到了我们的匹配项，一种为摘要分配分数的方法
Now that we found our matches, one way to assign a score

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是计算 unigram 的被召回次数。
to the summary is to compute the recall of the unigrams.

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这意味着我们只计算
This means we just count the number

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生成摘要和参考摘要的匹配词
of matching words in the generated

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并将计数通过除以参考文本中的单词数
and reference summaries and normalize the count

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进行规范化处理。
by dividing by the number of words in the reference.

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在这个例子中，我们找到了六个匹配的词
In this example, we found six matching words

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我们的参考文本有六个词。
and our reference has six words.

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所以我们的 unigram 召回是完美的。
So our unigram recall is perfect.

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这意味着在参考摘要中
This means that all of the words

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的所有的词都会出现
in the reference summary have been produced

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在生成的摘要中。
in the generated one.

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现在，完美的召回听起来不错，但想象一下
Now, perfect recall sounds great, but imagine

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如果我们生成的摘要是这样的
if our generated summary have been something like

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我真的，真的，真的，
I really, really, really,

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真的很喜欢阅读 Hunger Games。
really loved reading the Hunger Games.

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这也会有完美的召回
This would also have perfect recall

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但可以说是一个更糟糕的总结，
but is arguably a worse summary,

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因为它很冗长。
since it is verbose.

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为了应对这些场景，
To deal with these scenarios,

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我们还可以计算精度，
we can also compute precision,

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在 ROUGE 上下文中，它衡量了
which in the ROUGE context measures how much

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在生成器摘要中具有多少相关性。
of the generator summary was relevant.

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在实际操作中，通常计算精度和召回率
In practice, both precision and recall are usually computed

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然后报告 F1 分数。
and then the F1 score is reported.

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现在我们可以改变比较的粒度
Now we can change the granularity

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将比较的对象由 unigram 改变为 bigram。
of the comparison by comparing bigrams instead of unigrams.

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使用 bigram，我们将句子
With bigrams, we chunk the sentence into pairs

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切分为成对的连续单词，
of consecutive words and then count how many pairs

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然后计算生成的摘要中有多少对出现在参考摘要中。
in the generated summary are present in the reference one.

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这给了我们 ROUGE-2 精确度和召回率
This gives us ROUGE-2 precision and recall

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正如我们所见，
which as we can see,

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低于之前的 ROUGE-1 分数。
is lower than the ROUGE-1 scores from earlier.

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现在，如果摘要很长，
Now, if the summaries are long,

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ROUGE-2 分数通常会很小
the ROUGE-2 scores will generally be small

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因为要匹配的 bios 更少。
because there are fewer bios to match.

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这也适用于重写式摘要。
And this is also true for abstractive summarization.

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所以通常会报告 ROUGE-1 和 ROUGE-2 分数。
So both ROUGE-1 and ROUGE-2 scores are usually reported.

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我们将讨论的最后一个 ROUGE 变体是 ROUGE L。
The last ROUGE variant we will discuss is ROUGE L.

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ROUGE L 不比较 ngram
ROUGE L doesn't compare ngrams

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而是将每个摘要视为一系列单词
but instead treats each summary as a sequence of words

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然后寻找最长的公共子序列或 LCS。
and then looks for the longest common subsequence or LCS.

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子序列是以相同的相对顺序
A subsequence is a sequence that appears

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出现的序列，但不一定是连续的。
in the same relative order, but not necessarily contiguous.

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所以在这个例子中，我喜欢阅读 Hunger Games，
So in this example, I loved reading the Hunger Games,

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是两个摘要之间最长的公共子序列。
is the longest common subsequence between the two summaries.

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而相比 ROUGE-1 或 ROUGE-2 
And the main advantage of ROUGE L

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ROUGE L 的主要优势是它不依赖于
over ROUGE-1 or ROUGE-2 is that it doesn't depend

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在连续的 n-gram 匹配上，
on consecutive n-gram matches, and so it tends

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所以它倾向于更准确地捕捉句子结构。
to capture sentence structure much more accurately.

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现在在 Dataset 库中
Now to compute ROUGE scores

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计算 ROUGE 分数很简单。
in the data sets library is very simple.

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你只需使用 load_metric 函数，
You just use the load_metric function,

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提供你的模型摘要以及参考摘要
provide your model summaries along with the references

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你可以开始了。
and you're good to go.

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计算的输出
The output from the calculation

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包含很多信息。
contains a lot of information.

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我们首先可以看到的是
The first thing we can see is that the confidence intervals

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每个 ROUGE 分数的置信区间
of each ROUGE score are provided

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每个 ROUGE 分数的置信区间。
in the low, mid and high fields.

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当比较两个或多个模型时，
This is really useful if you wanna know the spread

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如果你想知道 ROUGE 分数的范围，这就真的很有用。
of your ROUGE scores when comparing two or more models.

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第二点要注意
The second thing to notice

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是我们有四种类型的 ROUGE 分数。
is that we have four types of ROUGE score.

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我们已经看过 ROUGE-1、ROUGE-2 和 ROUGE-L
We've already seen ROUGE-1, ROUGE-2 and ROUGE-L

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那么什么是 ROUGE-L sum 呢？
So what is ROUGE-L sum?

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其实就是 ROUGEL 的总和
Well, the sum in ROUGEL's sum

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指的是这个指标是
refers to the fact that this metric is computed

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当 ROUGE-L 作为单个句子的平均值计算时，
over a whole summary while ROUGE-L is computed

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基于整个摘要计算出来的。
as the average of individual sentences.

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（徽标呼啸而过）
(logo whooshing)

