text: • Samples belonging to domain classes with 
  less than 500 samples were removed, which 
  leads us to keep only 10 classes. 
  
  • Subsampling was performed on 2 classes con- 
  taining more than 10k samples each to lower 
  the number of samples and mitigate the unbal- 
  ance of the dataset. 
  
  More details about this process are provided in the 
  appendix A.2 along with some extracts in Figure 
  6. We make the dataset publicly available in both 
  their raw and clean versions. We use this dataset in 
  a clustering setup to cluster publications by their 
  title and use the domain as ground truth. To ensure 
  the quality of this dataset, we run 3 baseline mod- 
  els for classification: TF-IDF + SVM, a fine-tuned 
  Camembert (Martin et al., 2019) and GPT-4 lever- 
  aging In-Context Learning (ICL). Furthermore, we 
  run one baseline model for topic modeling: Latent 
  Dirichlet Allocation (LDA) (Blei et al., 2003) and 
  report scores in the appendix A.2. 
  
  3.1.3 SummEvalFr (Summarization) 
  The original SummEval dataset (Fabbri et al., 2021) 
  consists of 100 news articles from the CNN/Dai- 