| import pytextrank | |
| import spacy | |
| import streamlit as st | |
| st.title("Extractive Text Summarization") | |
| nlp = spacy.load("en_core_web_sm") | |
| nlp.add_pipe("textrank") | |
| input= st.text_area("Input text to summarize") | |
| user_limit=int(len(input.split("."))/5) | |
| doc=nlp(input) | |
| output="" | |
| if st.button("Summarize"): | |
| for i in doc._.textrank.summary(limit_sentences=user_limit): | |
| a=i.text | |
| output=output+a | |
| st.markdown(output) | |
| st.text("Length of Article="+str(len(input.split()))+" words") | |
| st.text("Length of summary="+str(len(output.split()))+" words") |