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| import streamlit as st | |
| st.set_page_config( | |
| page_title="Hello", | |
| page_icon="👋", | |
| ) | |
| st.markdown("# Welcome to Text Summarization Demo! 👋") | |
| st.sidebar.success("Select a demo above.") | |
| st.markdown("By Aaron Tae, Kevin Hamakawa, Emily Gong, Emily Huang, Tony Lei, Ved Phadke, Vivian Lee, Victor Shi") | |
| st.markdown("## Introduction") | |
| st.markdown(''' | |
| For our project, we looked into text summarization. Based on our research, there are 2 main categories of text summarization techniques: *extractive* and *abstractive*. As the names suggest, | |
| the *extractive summarization* method directly extracts information from the orignal text whereas the *abstractive summarization* | |
| method employs abstraction to produce a high level summary similar to humans'. | |
| ''') | |
| st.markdown("## Feel free to visit the tabs on the left to learn more") | |
| st.markdown('Find us on [GitHub](https://github.com/the-data-science-union/DSU-S2023-Text-Summarization) or [Medium](https://medium.com/@ucladsu/text-summarization-42a4e31b453f)') | |
| st.balloons() | |