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| 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 | |