---
language:
- en
tags: summarization
datasets:
- xsum
metrics:
- rouge
widget:
- 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."

---

### Pegasus for Financial Summarization 

This model was trained on a novel financial dataset which consists of 2K financial and economic articles from the [Bloomberg](https://www.bloomberg.com/europe) website of different categories such as stock, markets, currencies, rate and cryptocurrences, using [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html). This model is fine-tuned on the [google/pegasus-xsum model](https://huggingface.co/google/pegasus-xsum).  

PEGASUS model 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). 


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

```Python
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(
    input_ids, 
    max_length=32, 
    num_beams=5, 
    early_stopping=True
)

# 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 |


## Citation

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.
> Towards Human-Centered Summarization: A Case Study on Financial News.
> In Proceedings of the Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) Workshop at EACL (to appear). 2O21.

BibTeX entry:

```
@inproceedings{humancentered2021,
  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 Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) Workshop at EACL },
  pages={N/A},
  year={2021}
}
```