Update text.py

text.py

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 sum_app_text_tab_1= """
-## The Summarization Task {style=text-align:center}
+<h3><center> The Summarization Task  </center></h3>
+
 The goal of text summarization is to condense long documents into summaries, while maintaining key information found within the original text document. This is one of the most challenging NLP tasks as it requires a range of abilities, such as understanding long passages and generating coherent text that captures the main topics in a document. However, when done well, text summarization is a powerful tool that can speed up various business processes by relieving the burden of domain experts to read long documents in detail.
 
 Text summarization methods can either be used as an extractive or abstractive model. An Extractive method does what it sounds like, it concatenates different important sentences or paragraphs without understanding the meaning of those parts. Extractive summarization does not create any new word phrases. For instance,  if you presented a page of text to an extractive model, it would just act as a text “highlighter”. However, Abstractive summarization generates text in a fashion that tries to guess the meaning in a summarised way of the page of text it is presented. It would put words together in a meaningful way and add the most important fact found in the text.