PEGASUS for Financial Summarization This model was fine tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies. It is based on the PEGASUS model and in particular PEGASUS fine tuned on the Extreme Summarization (XSum) dataset: google/pegasus xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre training with Extracted Gap sentences for Abstractive Summarization. Note: This model serves as a base version. For an even more advanced model with significantly enhanced performance, please check out our advanced version on Rapid API. The advanced model offers more than a 16% increase in ROUGE scores (similarity to a human generated summary) compared to our base model. Moreover, our advanced model also offers several convenient plans tailored to different use cases and workloads, ensuring a seamless experience for both personal and enterprise access. How to use We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch. Evaluation Results The results before and after the fine t…
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