--- language: - en tags: summarization datasets: - xsum metrics: - rouge widget: - text: "National Commercial Bank (NCB), Saudi Arabia\u2019s 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\u2019s 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\u2019\ s third-largest lender. The entity\u2019s $46 billion market capitalization nearly\ \ matches that of Qatar National Bank QPSC, which is still the Middle East\u2019\ s biggest lender with about $268 billion of assets." model-index: - name: human-centered-summarization/financial-summarization-pegasus results: - task: type: summarization name: Summarization dataset: name: xsum type: xsum config: default split: test metrics: - name: ROUGE-1 type: rouge value: 35.2055 verified: true - name: ROUGE-2 type: rouge value: 16.5689 verified: true - name: ROUGE-L type: rouge value: 30.1285 verified: true - name: ROUGE-LSUM type: rouge value: 30.1706 verified: true - name: loss type: loss value: 2.7092134952545166 verified: true - name: gen_len type: gen_len value: 15.1414 verified: true --- ### PEGASUS for Financial Summarization 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. 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). ### 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. 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: ``` @inproceedings{passali-etal-2021-towards, 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", } ``` ## Support 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! More information about Medoid AI: - Website: [https://www.medoid.ai](https://www.medoid.ai) - LinkedIn: [https://www.linkedin.com/company/medoid-ai/](https://www.linkedin.com/company/medoid-ai/)