Update text.py

text.py

@@ -7,7 +7,7 @@ Text summarization methods can either be used as an extractive or abstractive mo
 
 <figure>
   <img src="file/EX_VS_ABS.png" alt="Trulli" style="width:100%">
-  <figcaption>Fig.1 - Trulli, Puglia, Italy.</figcaption>
+  <figcaption style="text-align: center;">Fig.1 - Trulli, Puglia, Italy.</figcaption>
 </figure>
 
 <h3><center> Abstractive Model </center></h3>
@@ -24,16 +24,22 @@ sum_app_text_tab_2= """
 <h2><center> 🤗  </center></h2>
 
 The figure below illustrates how the skill level of the model increases at each step:
-![alt text2](file/BART_SEQ.png)
-<center><i>image_caption</i></center>
+
+<figure>
+  <img src="file/BART_SEQ.png" alt="Trulli" style="width:100%">
+  <figcaption style="text-align: center;">Fig.1 - Trulli, Puglia, Italy.</figcaption>
+</figure>
 
 The main benefits of transfer learning in general include the saving of resources and improved efficiency when training new models, so feel free to adopt this model for your type of problem! 
 
 <h3><center> Extractive Model  </center></h3>
 
 The extractive models for this app are using sentence-transformer models, which basically is a bi-encoder that determines how similar two sentences are. This type of models convert texts into vectors (embedding) that capture semantic information. Additionally, LexRank, an unsupervised graph-based algorithm, is used to determine centrality scores as a post-process step to summarise. The main idea is that sentences "recommend" other similar sentences to the reader. Thus, if one sentence is very similar to many others, it will likely be a sentence of great importance. The importance of this sentence also stems from the importance of the sentences "recommending" it. Thus, to get ranked highly and placed in a summary, a sentence must be similar to many sentences that are in turn also similar to many other sentences. 
-![alt text3](file/Lex_rank.png)
-<center><i>image_caption</i></center>
+
+<figure>
+  <img src="file/Lex_rank.png)" alt="Trulli" style="width:100%">
+  <figcaption style="text-align: center;">Fig.1 - Trulli, Puglia, Italy.</figcaption>
+</figure>
 
 The figure above showcase how LexRank formats similarity graphs based on all possible sentence combinations sentence similarity from the vector embeddings. Notice that the most "recommended" sentences that are extracted (the right graph) are derived from a threshold value which filters "weaker" connections in the similarity graph.
 For more information on this topic read: [LexRank](https://www.aaai.org/Papers/JAIR/Vol22/JAIR-2214.pdf)