led-base-book-summary
Maintainer: pszemraj
56
📈
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The led-base-book-summary model is a fine-tuned version of the Longformer Encoder-Decoder (LED) model that has been optimized for summarizing long narratives, articles, papers, textbooks, and other lengthy documents. It was developed by pszemraj and is available through the Hugging Face model hub.
Compared to similar summarization models like led-large-book-summary, long-t5-tglobal-base-16384-book-summary, and text_summarization, the led-base-book-summary model is the smallest and fastest BookSum-tuned variant. While it may not generate the highest quality summaries, it offers a more efficient and accessible option for summarizing long-form text.
Model inputs and outputs
Inputs
- Long-form text, such as articles, papers, books, or other lengthy documents
Outputs
- Concise, coherent summaries that capture the key points and insights from the input text
Capabilities
The led-base-book-summary model excels at condensing extensive technical, academic, and narrative content into succinct, insightful summaries. It is particularly well-suited for generating "sparknotes-esque" explanations that offer a high-level overview of long-form material.
What can I use it for?
The led-base-book-summary model could be useful for a variety of applications that involve summarizing lengthy documents, such as:
- Generating summaries of research papers, technical reports, or academic textbooks to aid in literature review and research tasks
- Creating concise overviews of news articles or blog posts to help readers quickly digest the key information
- Providing summaries of books or other long-form narratives to give readers a high-level understanding of the content
Things to try
One interesting aspect of the led-base-book-summary model is its ability to generate "explanatory" summaries that go beyond simply extracting the most important points. By leveraging the sparknotes-style summarization approach, you can experiment with using the model to produce insightful, narrative-driven summaries that provide more than just a bullet-point list of key facts.
Additionally, you can try fine-tuning the model further on your own dataset or domain-specific content to see if you can improve the relevance and quality of the summaries for your particular use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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