Minor text changes

app.py

@@ -307,29 +307,29 @@ st.title('📜 Summarization fact checker 📜')
 st.header("🧑‍🏫 Introduction")
 
 introduction_checkbox = st.checkbox("Show introduction text", value=True)
-if introduction_checkbox:
-    st.markdown("""
-    Recent work using 🤖 **transformers** 🤖 on large text corpora has shown great success when fine-tuned on 
-    several different downstream NLP tasks. One such task is that of text summarization. The goal of text summarization 
-    is to generate concise and accurate summaries from input document(s). There are 2 types of summarization:
-    
-     - **Extractive summarization** merely copies informative fragments from the input
-     - **Abstractive summarization** may generate novel words. A good abstractive summary should cover principal 
-        information in the input and has to be linguistically fluent. This interactive blogpost will focus on this more difficult task of 
-        abstractive summary generation. Furthermore we will focus on factual errors in summaries, and less sentence fluency.""")
-
-    st.markdown("###")
-    st.markdown("🤔 **Why is this important?** 🤔 Let's say we want to summarize news articles for a popular "
-                "newspaper. If an article tells the story of **Putin** invading Ukraine, we don't want our summarization "
-                "model to say that **Biden** is invading Ukraine. Summarization could also be done for financial reports "
-                "for example. In such environments, these errors can be very critical, so we want to find a way to "
-                "detect them.")
-    st.markdown("###")
-    st.markdown("""To generate summaries we will use the 🐎 [PEGASUS](https://huggingface.co/google/pegasus-cnn_dailymail) 🐎
-    model, producing abstractive summaries from large articles. These summaries often contain sentences with different 
-    kinds of errors. Rather than improving the core model, we will look into possible post-processing steps to detect errors 
-    from the generated summaries. Throughout this blog, we will also explain the results for some methods on specific 
-    examples. These text blocks will be indicated and they change according to the currently selected article.""")
+#if introduction_checkbox:
+st.markdown("""
+Recent work using 🤖 **transformers** 🤖 on large text corpora has shown great success when fine-tuned on 
+several different downstream NLP tasks. One such task is that of text summarization. The goal of text summarization 
+is to generate concise and accurate summaries from input document(s). There are 2 types of summarization:
+
+ - **Extractive summarization** merely copies informative fragments from the input
+ - **Abstractive summarization** may generate novel words. A good abstractive summary should cover principal 
+    information in the input and has to be linguistically fluent. This interactive blogpost will focus on this more difficult task of 
+    abstractive summary generation. Furthermore we will focus on factual errors in summaries, and less sentence fluency.""")
+
+st.markdown("###")
+st.markdown("🤔 **Why is this important?** 🤔 Let's say we want to summarize news articles for a popular "
+            "newspaper. If an article tells the story of Elon Musk buying **Twitter**, we don't want our summarization "
+            "model to say that he bought **Facebook** instead. Summarization could also be done for financial reports "
+            "for example. In such environments, these errors can be very critical, so we want to find a way to "
+            "detect them.")
+st.markdown("###")
+st.markdown("""To generate summaries we will use the 🐎 [PEGASUS](https://huggingface.co/google/pegasus-cnn_dailymail) 🐎
+model, producing abstractive summaries from large articles. These summaries often contain sentences with different 
+kinds of errors. Rather than improving the core model, we will look into possible post-processing steps to detect errors 
+from the generated summaries. Throughout this blog, we will also explain the results for some methods on specific 
+examples. These text blocks will be indicated and they change according to the currently selected article.""")
 
 # GENERATING SUMMARIES PART
 st.header("🪶 Generating summaries")
@@ -356,7 +356,7 @@ if summarize_button:
     st.markdown("####")
     st.markdown(
         "*Below you can find the generated summary for the article. We will discuss two approaches that we found are "
-        "able to detect some common errors. Based on errors, one could then score different summaries, indicating how "
+        "able to detect some common errors. Based on these errors, one could then score different summaries, indicating how "
         "factual a summary is for a given article. The idea is that in production, you could generate a set of "
         "summaries for the same article, with different parameters (or even different models). By using "
         "post-processing error detection, we can then select the best possible summary.*")
@@ -394,7 +394,7 @@ if summarize_button:
                 "should also be present in the article. Thus we can extract all entities from the summary and compare "
                 "them to the entities of the original article, spotting potential hallucinations. The more unmatched "
                 "entities we find, the lower the factualness score of the summary. ")
-    with st.spinner("Calculating and matching entities..."):
+    with st.spinner("Calculating and matching entities, this takes about 10-20 seconds..."):
         entity_match_html = highlight_entities()
         st.markdown("####")
         st.write(entity_match_html, unsafe_allow_html=True)
@@ -485,7 +485,7 @@ if summarize_button:
         "will hopefully be at the top. Summaries with the same scores will get the same rank displayed.")
     st.markdown("####")
 
-    with st.spinner("Calculating more summaries and scoring them, this might take while..."):
+    with st.spinner("Calculating more summaries and scoring them, this might take a minute or two..."):
         summaries_list = []
         deduction_points = []
         # ENTITIES