Update app.py

app.py

@@ -272,7 +272,7 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                      gr.inputs.Slider(minimum=5, maximum=30, step=1, default=10, label="Max Keywords")], 
              outputs=gr.outputs.Dataframe(type="auto", label="dataframe",max_cols=None, max_rows=10, overflow_row_behaviour="paginate"), 
              theme="dark-peach", 
-             title="PubMed Abstract Retriever", description="Generates the keyphrases from an article which best describes the article.",
+             title="PubMed Abstract Retriever", description="Retrieves relevant PubMed abstracts for an online article which can be used as further references.",
              article= "This work is based on the paper <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>provided here</a>." 
              "\t It uses the TextRank algorithm with SBERT to first find the top sentences and then extracts the keyphrases from those sentences using scispaCy and SBERT."
              "\t The application then uses a UMLS based BERT model, <a href=https://arxiv.org/abs/2010.11784>SapBERT</a> to cluster the keyphrases using K-means clustering method and finally create a boolean query. After that the top 20 titles and abstracts are retrieved from PubMed database and displayed according to relevancy. "