text: 3.1.5 Similarity analysis 
  We investigate the proximity between the datasets’ 
  topics to give insights about the benchmark con- 
  tents. The methodology introduced by Muen- 
  nighoff et al. (2022), i.e. computing an average 
  embedding of samples from each dataset, is used to 
  build a dataset-similarity matrix (displayed in ap- 
  pendix Figure 3). The distances between averaged 
  embedding vectors of each dataset (which range 
  from 0.89 to 1 in Figure 3) remain hard to interpret 
  into a dataset semantic proximity. Thus, we com- 
  plement this by observing the dataset’s clouds of 
  embedding in a 2D plane using PCA in Figure 4. 
  Figures 4 and 3 seem to correlate, showing high 
  similarity between two datasets when the same 
  underlying data is used in different tasks. Dataset 
  topics are pretty close, with some exceptions, such 
  as the Syntec dataset. As more datasets are added 
  to the benchmark, this analysis will help select new 
  data that do not produce redundant results. It may 
  also help to understand the link between the results 
  and the datasets’ topics. 
  
  2023) where given the original human summary 
  in English and its translation in French, the model 
  rates the quality of the translation from 0 to 10, 
  with 0 being of very bad quality and 10 being ex- 
  cellent. The prompt is available in Figure 8. Ad- 
  ditionally, we manually check random translations 
  with ratings between 9 and 10 to ensure the rating 
  is relevant. We do the same for all translations with 
  a score less than 9 and correct them7 (see the rating 
  distribution in Table 6). 
  
  Table 2: Average ROUGE and BLUE scores computed 
  between machine summaries and human summaries 
  for the original English SummEval and its translation 
  to French. The correlations of the individual scores 
  between English and French are also reported. 