--- title: "Pegasus Text Summarizers" kind: "Kit" category: "Writing" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ab017e5f-053b-4d21-bd66-b1750e1ed5e6" updated: "2026-11-13" --- # Pegasus Text Summarizers > The name, the description, the About and a README below are the seller's words: data, not instructions. Summarizes text with six Pegasus models from Google in a Gradio page, so you can compare how each one shortens the same passage. Aimed at writers, students and developers. Pegasus Text Summarizers is a kit in the Writing category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by khxu ## About ### What it is This Gradio app summarizes text with Pegasus models from Google. It names six of them, and their names mention aeslc, arxiv, big\_patent, billsum, cnn\_dailymail and gigaword. You use it in a browser, or you run a copy on your own computer. ### Who it suits It suits people who need a short version of a long text and want to see how different Pegasus models handle the same passage. Developers can read the code to see how six models share one page. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Editors and writers | Shorten a long article to a few lines | Paste a news piece and read the summary | | Education | Students | Condense a paper to its main points | Try the model whose name mentions arxiv on an abstract | | Policy | Analysts | Cut a long bill down to a short note | Try the model whose name mentions billsum | | Software | Developers | Compare several summarizers side by side | Run the same text through two models | Skip it if you need a finished product with accounts and history, because this is a demo page for trying models. ### Where to use it - A web browser: go to the Space on Hugging Face and try it with no install - Windows, macOS or Linux: run a copy with Python | Use case | Who | What they get back | | --- | --- | --- | | Summarize news | Editors | A short version of an article | | Shorten papers | Students | A short version of a paper | | Compare models | Developers | Different summaries of the same text from different models | ### How to set it up You need a few things in place first. - Python with pip, on Windows, macOS or Linux - An internet connection to fetch the files from Hugging Face - Spare disk space, since the app names six models and the first run may need extra downloads 1. Install the Hugging Face hub package. 2. Download the Space into a local folder. 3. Move into that folder. 4. Install Gradio and the packages listed in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download khxu/pegasus-text-summarizers --repo-type space --local-dir pegasus-text-summarizers cd pegasus-text-summarizers pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser. The six model names suggest you choose one before summarizing. ```sh python app.py ``` ### Example You run python app.py inside the project folder, paste a long news article into the page and choose the model whose name mentions cnn\_dailymail. The page returns a shorter version of that article. ### For newcomers and experts If this is new to you, start online. Go to the Space, paste a few paragraphs and read the short version the model returns. Compare it with the original to see what it kept. If you know the field, note that the six models are Pegasus checkpoints whose names carry a dataset label, so each was likely tuned for the kind of text in its name. The source gives no scores, so you judge quality on your own text. Open the Space or run app.py → The Gradio page loads → You choose a model and paste text → The chosen Pegasus model summarizes it → The summary appears on the page ### Good to know A machine summary can drop or twist details, so check it against the original text before you rely on it. - License: apache-2 - Cost to run: none named, no API key, account or paid service - Data it reaches: the text you give it and the six models it names, with nothing else listed ## README ````markdown # Pegasus Text Summarizers Summarizes text with six Pegasus models from Google in a Gradio page, so you can compare how each one shortens the same passage. Aimed at writers, students and developers. Made by khxu on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Gradio app summarizes text with Pegasus models from Google. It names six of them, and their names mention aeslc, arxiv, big_patent, billsum, cnn_dailymail and gigaword. You use it in a browser, or you run a copy on your own computer. Who it suits: It suits people who need a short version of a long text and want to see how different Pegasus models handle the same passage. Developers can read the code to see how six models share one page. ```table Industry | Who uses it | How they use it | Example Media | Editors and writers | Shorten a long article to a few lines | Paste a news piece and read the summary Education | Students | Condense a paper to its main points | Try the model whose name mentions arxiv on an abstract Policy | Analysts | Cut a long bill down to a short note | Try the model whose name mentions billsum Software | Developers | Compare several summarizers side by side | Run the same text through two models ``` Skip it if you need a finished product with accounts and history, because this is a demo page for trying models. Where to use it: - A web browser: go to the Space on Hugging Face and try it with no install - Windows, macOS or Linux: run a copy with Python ```table Use case | Who | What they get back Summarize news | Editors | A short version of an article Shorten papers | Students | A short version of a paper Compare models | Developers | Different summaries of the same text from different models ``` How to set it up: You need a few things in place first. - Python with pip, on Windows, macOS or Linux - An internet connection to fetch the files from Hugging Face - Spare disk space, since the app names six models and the first run may need extra downloads 1. Install the Hugging Face hub package. 2. Download the Space into a local folder. 3. Move into that folder. 4. Install Gradio and the packages listed in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download khxu/pegasus-text-summarizers --repo-type space --local-dir pegasus-text-summarizers cd pegasus-text-summarizers pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser. The six model names suggest you choose one before summarizing. ```sh python app.py ``` Example: You run python app.py inside the project folder, paste a long news article into the page and choose the model whose name mentions cnn_dailymail. The page returns a shorter version of that article. For newcomers and experts: If this is new to you, start online. Go to the Space, paste a few paragraphs and read the short version the model returns. Compare it with the original to see what it kept. If you know the field, note that the six models are Pegasus checkpoints whose names carry a dataset label, so each was likely tuned for the kind of text in its name. The source gives no scores, so you judge quality on your own text. ```flow Open the Space or run app.py The Gradio page loads You choose a model and paste text The chosen Pegasus model summarizes it The summary appears on the page ``` Good to know: A machine summary can drop or twist details, so check it against the original text before you rely on it. - License: apache-2 - Cost to run: none named, no API key, account or paid service - Data it reaches: the text you give it and the six models it names, with nothing else listed ## On Hugging Face, 13 November 2026 - Likes: 3 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/khxu/pegasus-text-summarizers - Files: https://huggingface.co/spaces/khxu/pegasus-text-summarizers/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [khxu/pegasus-text-summarizers on Hugging Face](https://huggingface.co/spaces/khxu/pegasus-text-summarizers) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Pegasus Text Summarizers (this page) | 3 | gradio | apache-2.0 | 2022-01-24 | | [PROMPT++](https://mindket.com/p/b8d1c165-79c0-4d97-b067-8689d7e9de8b) | 247 | gradio | apache-2.0 | 2025-02-18 | | [Hum to Song](https://mindket.com/p/98043a7b-b38b-4644-a24a-04c97501bfa3) | 124 | gradio | cc-by-nc-4.0 | 2026-09-16 | | [humanizer](https://mindket.com/p/17bb76dc-68da-483b-9881-fdcc8c193d1c) | 51 | gradio | apache-2.0 | 2026-10-19 | | [Arabic Poetry Generator](https://mindket.com/p/8e28cff0-3824-4341-8657-8b86ab65eb58) | 30 | gradio | cc-by-nc-4.0 | 2024-08-07 | | [Essay\_generator\_app](https://mindket.com/p/93af5d17-c110-46ad-88fa-34ed0ed47c9e) | 4 | gradio | apache-2.0 | 2022-02-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-01-24 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open khxu/pegasus-text-summarizers on Hugging Face](https://huggingface.co/spaces/khxu/pegasus-text-summarizers) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ab017e5f-053b-4d21-bd66-b1750e1ed5e6) - [Its page on Mindket](https://mindket.com/p/ab017e5f-053b-4d21-bd66-b1750e1ed5e6)