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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.

How to use

We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.

from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration

# Let's load the model and the tokenizer 
model_name = "human-centered-summarization/financial-summarization-pegasus"
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name) # If you want to use the Tensorflow model 
                                                                    # just replace with TFPegasusForConditionalGeneration

# Some text to summarize here
text_to_summarize = "National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region’s third-largest lender. The entity’s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion of assets."

# Tokenize our text
# If you want to run the code in Tensorflow, please remember to return the particular tensors as simply as using return_tensors = 'tf'
input_ids = tokenizer(text_to_summarize, return_tensors="pt").input_ids

# Generate the output (Here, we use beam search but you can also use any other strategy you like)
output = model.generate(

# Finally, we can print the generated summary
print(tokenizer.decode(output[0], skip_special_tokens=True))
# Generated Output: Saudi bank to pay a 3.5% premium to Samba share price. Gulf region’s third-largest lender will have total assets of $220 billion

Evaluation Results

The results before and after the fine-tuning on our dataset are shown below:

Fine-tuning R-1 R-2 R-L R-S
Yes 23.55 6.99 18.14 21.36
No 13.8 2.4 10.63 12.03


You can find more details about this work in the following workshop paper. If you use our model in your research, please consider citing our paper:

T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. 2021. Towards Human-Centered Summarization: A Case Study on Financial News. In Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing(pp. 21–27). Association for Computational Linguistics.

BibTeX entry:

    title = "Towards Human-Centered Summarization: A Case Study on Financial News",
    author = "Passali, Tatiana  and Gidiotis, Alexios  and Chatzikyriakidis, Efstathios  and Tsoumakas, Grigorios",
    booktitle = "Proceedings of the First Workshop on Bridging Human{--}Computer Interaction and Natural Language Processing",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.hcinlp-1.4",
    pages = "21--27",


Contact us at info@medoid.ai if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!

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