## 8 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Reuters** & **Ohsumed** & **20 Newsgroups** & **BBC** & **BBCSport** \\ \hline BoW & **83.42**, \(\sigma=0.0\) & **60.25**, \(\sigma=0.0\) & 71.72, \(\sigma=0.0042\) & 97.03, \(\sigma=0.0077\) & 96.88, \(\sigma=0.0127\) \\ TFIDF & 75.58, \(\sigma=0.0\) & 56.44, \(\sigma=0.0\) & **85.55**, \(\sigma=0.0008\) & **97.84**, \(\sigma=0.0037\) & **98.51**, \(\sigma=0.0117\) \\ \hline LSA & 64.12, \(\sigma=0.0017\) & 35.72, \(\sigma=0.0012\) & 71.76, \(\sigma=0.0053\) & 96.90, \(\sigma=0.0030\) & 97.83, \(\sigma=0.0117\) \\ LDA & 52.91, \(\sigma=0.0093\) & 20.07, \(\sigma=0.0036\) & 60.90, \(\sigma=0.0102\) & 93.08, \(\sigma=0.0056\) & 92.80, \(\sigma=0.0419\) \\ P-SIF & 62.61, \(\sigma=0.0016\) & 34.33, \(\sigma=0.0015\) & 63.41, \(\sigma=0.0057\) & 95.33, \(\sigma=0.0063\) & 94.70, \(\sigma=0.0267\) \\ Doc2 Vec-DM & 79.81, \(\sigma=0.0013\) & 58.66, \(\sigma=0.0021\) & 75.25, \(\sigma=0.0075\) & 96.63, \(\sigma=0.0071\) & 97.29, \(\sigma=0.0096\) \\ Doc2 Vec-DBoW & 82.65, \(\sigma=0.0006\) & 59.48, \(\sigma=0.0006\) & 82.52, \(\sigma=0.0040\) & 97.35, \(\sigma=0.0046\) & 97.96, \(\sigma=0.0096\) \\ [PERSON] & 81.33, \(\sigma=0.004\) & 54.39, \(\sigma=0.0029\) & **89.57**, \(\sigma=0.0066\) & **99.24**, \(\sigma=0.0046\) & **100**, \(\sigma=0.0\) \\ HWAN & 82.85, \(\sigma=0.0022\) & 55.64, \(\sigma=0.0027\) & **88.68**, \(\sigma=0.0030\) & **99.69**, \(\sigma=0.0011\) & **100**, \(\sigma=0.0\) \\ Longformer base & 79.90, \(\sigma=0.0042\) & 53.88, \(\sigma=0.0077\) & 81.33, \(\sigma=0.0054\) & **98.20**, \(\sigma=0.0028\) & **99.32**, \(\sigma=0.0061\) \\ \hline SOTA & 97.44 [37] & 75.86 [47] & 88.6 [48] & 99.59 [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Averaged overall classification accuracy (in percent); cross-validation for Ohsumed, BBC and BBCSport, test-train split for Reuters and 20 Newsgroups. The maximum length of input data for HAN and HWAN was set at 30 utterances 300 words each. Numbers in bold denotes results better than BoW and TFIDF or the best result. Moreover, with this model, it will be possible to obtain good results while keeping the size of the neural network small. Table 2 shows the trend in model training time. The BoW and TFIDF models, which rely on word counts in the training dataset, take the shortest amount of time to train. Statistical models, such as LSA and LDA, take much longer due to the fact that they perform singular value decomposition and Gibbs Sampling on matrices of features. As the complexity and size of the model increases, so does the training time, which is why the Longformer model takes the longest time to train of the chosen methods, and requires the most resources. Comparing the training times of the TFIDF model and Longformer, we observe an increase by about 25,000-80,000 times. It is more efficient to train the HAN model, which needs about 2000 times more time to train, and HWAN--about 6000 times more. The doc2 vec models require around 100-200 times more time to train compared to TFIDF. The summary of our comparative study of representations is presented in Table 6, where we indicate the pros and cons of each method.