Spaces:
Runtime error
A newer version of the Streamlit SDK is available:
1.41.1
title: Content Summarizer
emoji: 🔥
colorFrom: purple
colorTo: green
sdk: streamlit
sdk_version: 1.17.0
app_file: app.py
pinned: false
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Content Summarizer
The Content Summarizer is a project that can generate summaries for various types of content including text, URLs, audio, video, and YouTube. It utilizes the transformers library and leverages the BART-large-CNN, T5-small and Whisper-tiny.en models to provide effective summarization.
It contains two options for summarization:
- Overall summary
- Auto-Chapters summary
Overall summary
The overall summary is generated using BART-large-CNN with chunk split algorithm.
Auto Chapters summary
In this type, the text content is split using clustering techniques and chunk split algorithm and uses BART-large-CNN and T5-small for summarization which gives blocks of summary with headings for each.
To run the app, install the packages from requirements.txt and execute the command streamlit run app.py from the root of this project.
This repository has also been added as a space in huggingface: https://huggingface.co/spaces/KevlarVK/content_summarizer