Update README.md

README.md

@@ -164,7 +164,7 @@ if __name__ == "__main__":
 ## Training data
 
 In order to train the model, it's transformers were trained with five datasets, which were:
-- Scientific Papers (arXiv + PubMed): Cohan et al. (2018) found out that there were only
+- **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
 extractive humam summaries. In order to fill the gap and to provide a dataset with long text
 documents for abstractive summarization, the authors compiled two new datasets with scientific
@@ -172,19 +172,19 @@ papers from arXiv and PubMed databases. Scientific papers are specially convenie
 desired kind of ATS the authors mean to achieve, and that is due to their large length and
 the fact that each one contains an abstractive summary made by its author – i.e., the paper’s
 abstract.
-- BIGPATENT: Sharma et al. (2019) introduced the BIGPATENT dataset that provides goods
+- **BIGPATENT**: Sharma et al. (2019) introduced the BIGPATENT dataset that provides goods
 examples for the task of abstractive summarization. The data dataset is built using Google
 Patents Public Datasets, where for each document there is one gold-standard summary which
 is the patent’s original abstract. One advantage of this dataset is that it does not present
 difficulties inherent to news summarization datasets, where summaries have a flattened discourse
 structure and the summary content arises in the begining of the document.
-- CNN Corpus: Lins et al. (2019b) introduced the corpus in order to fill the gap that most news
+- **CNN Corpus**: Lins et al. (2019b) introduced the corpus in order to fill the gap that most news
 summarization single-document datasets have fewer than 1,000 documents. The CNN-Corpus
 dataset, thus, contains 3,000 Single-Documents with two gold-standard summaries each: one
 extractive and one abstractive. The encompassing of extractive gold-standard summaries is
 also an advantage of this particular dataset over others with similar goals, which usually only
 contain abstractive ones.
-- CNN/Daily Mail: Hermann et al. (2015) intended to develop a consistent method for what
+- **CNN/Daily Mail**: Hermann et al. (2015) intended to develop a consistent method for what
 they called ”teaching machines how to read”, i.e., making the machine be able to comprehend a
 text via Natural Language Processing techniques. In order to perform that task, they collected
 around 400k news from the newspapers CNN and Daily Mail and evaluated what they considered
@@ -192,7 +192,7 @@ to be the key aspect in understanding a text, namely the answering of somewhat c
 questions about it. Even though ATS is not the main focus of the authors, they took inspiration
 from it to develop their model and include in their dataset the human made summaries for each
 news article.
-- XSum: Narayan et al. (2018b) introduced the single-document dataset, which focuses on a
+- **XSum**: Narayan et al. (2018b) introduced the single-document dataset, which focuses on a
 kind of summarization described by the authors as extreme summarization – an abstractive
 kind of ATS that is aimed at answering the question “What is the document about?”. The data
 was obtained from BBC articles and each one of them is accompanied by a short gold-standard
@@ -200,8 +200,11 @@ summary often written by its very author.
 
 ## 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