| ### 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). | |
| #### Installing | |
| In order to use this model, you have to install Transformers and SentencePiece Tokenizer as follows: | |
| ```Python | |
| pip install transformers | |
| ``` | |
| #### How to use | |
| We provide a simple snippet of how to use this model for the task of financial summarization in Pytorch. Don't worry if you prefer | |
| Tensorflow, you can also run the following code only with minor changes. | |
| ```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 BankNational 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 | | |