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--- |
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language: |
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- en |
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tags: summarization |
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datasets: |
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- xsum |
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metrics: |
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- rouge |
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widget: |
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- text: "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." |
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--- |
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### PEGASUS for Financial Summarization |
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This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from [Bloomberg](https://www.bloomberg.com/europe), on topics such as stock, markets, currencies, rate and cryptocurrencies. |
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It is based on the [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html) model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset:[google/pegasus-xsum model](https://huggingface.co/google/pegasus-xsum). 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](https://arxiv.org/pdf/1912.08777.pdf). |
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### How to use |
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We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch. |
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```Python |
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from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration |
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# Let's load the model and the tokenizer |
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model_name = "human-centered-summarization/financial-summarization-pegasus" |
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tokenizer = PegasusTokenizer.from_pretrained(model_name) |
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model = PegasusForConditionalGeneration.from_pretrained(model_name) # If you want to use the Tensorflow model |
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# just replace with TFPegasusForConditionalGeneration |
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# Some text to summarize here |
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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." |
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# Tokenize our text |
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# If you want to run the code in Tensorflow, please remember to return the particular tensors as simply as using return_tensors = 'tf' |
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input_ids = tokenizer(text_to_summarize, return_tensors="pt").input_ids |
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# Generate the output (Here, we use beam search but you can also use any other strategy you like) |
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output = model.generate( |
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input_ids, |
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max_length=32, |
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num_beams=5, |
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early_stopping=True |
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) |
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# Finally, we can print the generated summary |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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# 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 |
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``` |
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## Evaluation Results |
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The results before and after the fine-tuning on our dataset are shown below: |
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| Fine-tuning | R-1 | R-2 | R-L | R-S | |
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|:-----------:|:-----:|:-----:|:------:|:-----:| |
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| Yes | 23.55 | 6.99 | 18.14 | 21.36 | |
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| No | 13.8 | 2.4 | 10.63 | 12.03 | |
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## Citation |
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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: |
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> T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. |
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> Towards Human-Centered Summarization: A Case Study on Financial News. |
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> In Proceedings of the Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) Workshop at EACL (to appear). 2O21. |
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BibTeX entry: |
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``` |
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@inproceedings{humancentered2021, |
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title={Towards Human-Centered Summarization: A Case Study on Financial News}, |
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author={Passali, Tatiana and Gidiotis, Alexios and Chatzikyriakidis, Efstathios and Tsoumakas, Grigorios}, |
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booktitle={Proceedings of the Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) Workshop at EACL }, |
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pages={N/A}, |
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year={2021} |
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} |
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``` |
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## Support |
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Contact us at [info@medoid.ai](mailto: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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