pegasus-cnn_dailymail
Maintainer: google
68
🛠️
| 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 |
Create account to get full access
Model overview
The pegasus-cnn_dailymail model is a member of the PEGASUS family of models, developed by Google researchers Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. It is a text summarization model trained on a mixture of the C4 and HugeNews datasets, with some additional modifications compared to the original pegasus-large model. The "Mixed & Stochastic" version of the model was trained for longer (1.5M steps vs. 500k) and used a variable gap sentence ratio between 15-45% during pretraining, as well as stochastic sampling of important sentences.
Model inputs and outputs
Inputs
- Text to be summarized
Outputs
- A concise summary of the input text, generated using an abstractive summarization approach.
Capabilities
The pegasus-cnn_dailymail model is capable of generating informative summaries of text across a variety of domains, including news articles, scientific papers, and more. Its performance has been evaluated on several benchmark datasets, where it has achieved state-of-the-art results, outperforming previous summarization models.
What can I use it for?
You can use the pegasus-cnn_dailymail model for a variety of text summarization tasks, such as quickly digesting long articles, generating concise summaries for business reports, or summarizing research papers. Its strong performance makes it a useful tool for anyone who needs to extract the key information from large amounts of text. Additionally, the model could be fine-tuned on domain-specific data to further improve its performance for particular use cases.
Things to try
One interesting aspect of the pegasus-cnn_dailymail model is its use of a variable gap sentence ratio during pretraining. This approach, which involves randomly masking out a portion of the sentences in the training corpus, helps the model learn to identify the most salient information in a document. You could experiment with adjusting this ratio or trying other pretraining techniques to see how they impact the model's summarization capabilities.
Another area to explore would be evaluating the model's performance on different types of text, beyond the news articles and scientific papers it was primarily trained on. Applying it to domains like legal documents, financial reports, or social media posts could yield interesting insights into its flexibility and generalization abilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🚀
pegasus-large
91
The pegasus-large model is a part of the Pegasus family of models developed by Google. Pegasus models are designed for the task of text summarization, aiming to generate concise summaries of long-form text. The pegasus-large model has been trained on a mixture of the C4 and HugeNews datasets, using a technique called "Mixed & Stochastic Checkpoints" which involves sampling gap sentence ratios and importance scores during training. This allows the model to better handle a variety of summarization tasks across different datasets. Model inputs and outputs Inputs Long-form text that needs to be summarized Outputs Concise summary of the input text Capabilities The pegasus-large model is capable of generating high-quality abstractive summaries across a wide range of datasets, including news articles, academic papers, and more. It outperforms previous state-of-the-art models on benchmark summarization tasks like XSUM, CNN/DailyMail, and Newsroom. The model's performance is further improved by the "Mixed & Stochastic" training technique, which allows it to handle a diverse set of summarization challenges. What can I use it for? The pegasus-large model can be used for a variety of text summarization tasks, such as automatically generating summaries of news articles, research papers, or lengthy documents. This can be particularly useful for applications like content curation, information retrieval, or knowledge distillation. The model's strong performance across multiple domains makes it a versatile tool for researchers and developers working on summarization-related projects. Things to try One interesting thing to try with the pegasus-large model is exploring its ability to handle different types of text, beyond the standard news and academic domains. For example, you could experiment with summarizing long-form creative writing, technical manuals, or even transcripts of spoken conversations. The model's robust performance suggests it may be able to adapt to a wide range of summarization challenges, and investigating its limitations and edge cases could lead to valuable insights.
Updated Invalid Date
📈
pegasus-xsum
161
The pegasus-xsum model is a pre-trained text summarization model developed by Google. It is based on the Pegasus (Pre-training with Extracted Gap-sentences for Abstractive Summarization) architecture, which uses a novel pre-training approach that focuses on generating important sentences as the summary. The model was trained on a large corpus of text data, including the C4 and HugeNews datasets, and has shown strong performance on a variety of summarization benchmarks. Compared to similar models like the mT5-multilingual-XLSum and pegasus-large models, the pegasus-xsum model has been specifically fine-tuned for the XSUM summarization dataset, which contains news articles. This specialized training allows the model to generate more concise and accurate summaries for this type of text. Model inputs and outputs Inputs Text**: The model takes in a single text input, which can be a news article, blog post, or other long-form text that needs to be summarized. Outputs Summary**: The model generates a concise summary of the input text, typically 1-3 sentences long. The summary aims to capture the key points and essential information from the original text. Capabilities The pegasus-xsum model excels at generating concise and informative summaries for news articles and similar types of text. It has been trained to identify and extract the most salient information from the input, allowing it to produce high-quality summaries that are both accurate and succinct. What can I use it for? The pegasus-xsum model can be particularly useful for applications that require automatic text summarization, such as: News and media aggregation**: Summarizing news articles or blog posts to provide users with a quick overview of the key information. Research and academic summarization**: Generating summaries of research papers, scientific articles, or other technical documents to help readers quickly understand the main points. Customer support and content curation**: Summarizing product descriptions, FAQs, or other support documentation to make it easier for customers to find the information they need. Things to try One interesting aspect of the pegasus-xsum model is its ability to generate summaries that are tailored to the specific input text. By focusing on extracting the most important sentences, the model can produce summaries that are both concise and highly relevant to the original content. To get the most out of this model, you could try experimenting with different types of input text, such as news articles, blog posts, or even longer-form academic or technical documents. Pay attention to how the model's summaries vary based on the characteristics and subject matter of the input, and see if you can identify any patterns or best practices for using the model effectively.
Updated Invalid Date
✨
financial-summarization-pegasus
117
The financial-summarization-pegasus model is a specialized language model fine-tuned on a dataset of financial news articles from Bloomberg. It is based on the PEGASUS model, which was originally proposed for the task of abstractive summarization. This model aims to generate concise and informative summaries of financial content, which can be useful for quickly grasping the key points of lengthy financial reports or news articles. Compared to similar models, the financial-summarization-pegasus model has been specifically tailored for the financial domain, which can lead to improved performance on that type of content compared to more general summarization models. For example, the pegasus-xsum model is a version of PEGASUS that has been fine-tuned on the XSum dataset for general-purpose summarization, while the text_summarization model is a fine-tuned T5 model for text summarization. The financial-summarization-pegasus model aims to provide specialized capabilities for financial content. Model Inputs and Outputs Inputs Financial news articles**: The model takes as input financial news articles or reports, such as those covering stocks, markets, currencies, rates, and cryptocurrencies. Outputs Concise summaries**: The model generates summarized text that captures the key points and important information from the input financial content. The summaries are designed to be concise and informative, allowing users to quickly grasp the essential details. Capabilities The financial-summarization-pegasus model excels at generating coherent and factually accurate summaries of financial news and reports. It can distill lengthy articles down to their core elements, highlighting the most salient information. This can be particularly useful for investors, analysts, or anyone working in the financial industry who needs to quickly understand the main takeaways from a large volume of financial content. What Can I Use It For? The financial-summarization-pegasus model can be leveraged in a variety of applications related to the financial industry: Financial news aggregation**: The model could be used to automatically summarize financial news articles from sources like Bloomberg, providing users with concise overviews of the key points. Financial report summarization**: The model could be applied to lengthy financial reports and earnings statements, helping analysts and investors quickly identify the most important information. Investment research assistance**: Portfolio managers and financial advisors could use the model to generate summaries of market analysis, economic forecasts, and other financial research, streamlining their decision-making processes. Regulatory compliance**: Financial institutions could leverage the model to quickly summarize regulatory documents and updates, ensuring they remain compliant with the latest rules and guidelines. Things to Try One interesting aspect of the financial-summarization-pegasus model is its potential to handle domain-specific terminology and jargon commonly found in financial content. Try feeding the model a complex financial report or article and see how well it is able to distill the key information while preserving the necessary technical details. You could also experiment with different generation parameters, such as adjusting the length of the summaries or trying different beam search configurations, to find the optimal balance between conciseness and completeness for your specific use case. Additionally, you may want to compare the performance of this model to the advanced version mentioned in the description, which reportedly offers enhanced performance through further fine-tuning.
Updated Invalid Date
⛏️
Randeng-Pegasus-523M-Summary-Chinese
50
The Randeng-Pegasus-523M-Summary-Chinese model is a large language model developed by IDEA-CCNL, a Chinese AI research institute. It is based on the PEGASUS architecture, which was originally proposed for text summarization tasks. This model has been fine-tuned on several Chinese text summarization datasets, making it well-suited for generating concise summaries of Chinese text. The model is part of the Randeng series of language models from IDEA-CCNL, which includes other large Chinese models like Wenzhong2.0-GPT2-3.5B-chinese and Randeng-T5-784M-MultiTask-Chinese. These models have been trained on large Chinese corpora and excel at various natural language tasks. Model inputs and outputs Inputs Text**: The Randeng-Pegasus-523M-Summary-Chinese model takes in Chinese text as its input, which it then summarizes. Outputs Summary**: The model generates a concise summary of the input text, capturing the key points and main ideas. Capabilities The Randeng-Pegasus-523M-Summary-Chinese model is particularly adept at generating high-quality text summaries in Chinese. It has been fine-tuned on a variety of Chinese text summarization datasets, allowing it to handle a wide range of topics and styles of text. What can I use it for? This model can be useful for a variety of applications that require summarizing Chinese text, such as news articles, research papers, or product descriptions. It could be integrated into content curation platforms, customer service chatbots, or research analysis tools to help users quickly digest and understand large amounts of information. Things to try One interesting thing to try with this model is to experiment with different input text lengths and styles to see how it handles summarizing longer or more complex documents. You could also try fine-tuning the model further on your own domain-specific text summarization datasets to see if you can improve its performance on your particular use case.
Updated Invalid Date