Update README.md

README.md

@@ -161,9 +161,14 @@ if __name__ == "__main__":
             evaluator.join_all_results()
 ```
 
-## Training data
+### Preprocessing
+[PERGUNTAR ARTHUR]
+
+Hey, look how easy it is to write LaTeX equations in here \\(Ax = b\\) or even $ Ax = b $
+## Datasets
 
-In order to train the model, it's transformers were trained with five datasets, which were:
+In order to evaluate the model, summaries were generated by each of its summarization methods, which
+used as source texts documents achieved from existing datasets. The chosen datasets for evaluation were the following:
 
 - **Scientific Papers (arXiv + PubMed)**: Cohan et al. (2018) found out that there were only
 datasets with short texts (with an average of 600 words) or datasets with longer texts with
@@ -199,18 +204,10 @@ kind of ATS that is aimed at answering the question “What is the document abou
 was obtained from BBC articles and each one of them is accompanied by a short gold-standard
 summary often written by its very author.
 
-Each of their documents was summarized through every summarization method applied in the code and evaluated 
-in comparison with the gold-standard summaries.
-
-## Training procedure
-
-
-### Preprocessing
-[PERGUNTAR ARTHUR]
-
-Hey, look how easy it is to write LaTeX equations in here \\(Ax = b\\) or even $ Ax = b $
 ## Evaluation results
 
+Each of the datasets' documents was summarized through every summarization method applied in the code and evaluated 
+in comparison with the gold-standard summaries.
 
 Table 2: Results from Pre-trained Longformer + ML models.