--- 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. 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: - type: rouge value: 35.2055 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA5OTZkY2YxMDU1YzE3NGJlMmE1OTg1NjlmNzcxOTg4YzY2OThlOTlkNGFhMGFjZWY4YjdiMjU5NDdmMWYzNSIsInZlcnNpb24iOjF9.ufBRoV2JoX4UlEfAUOYq7F3tZougwngdpKlnaC37tYXJU3omsR5hTsWM69hSdYO-k0cKUbAWCAMzjmoGwIaPAw - type: rouge value: 16.5689 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQwMmM2NjJjNzM1N2Y3NjZmMmE5NzNlNjRjNjEwNzNhNjcyZTRiMGRlODY3NWUyMGQ0YzZmMGFhODYzOTRmOSIsInZlcnNpb24iOjF9.AZZkbaYBZG6rw6-QHYjRlSl-p0gBT2EtJxwjIP7QYH5XIQjeoiQsTnDPIq25dSMDbmQLSZnpHC104ZctX0f_Dg - type: rouge value: 30.1285 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTRjYThlMTllZjI4MGFiMDZhZTVkYmRjMTNhZDUzNTQ0OWQyNDQxMmQ5ODJiMmJiNGI3OTAzYjhiMzc2MTI4NCIsInZlcnNpb24iOjF9.zTHd3F4ZlgS-azl-ZVjOckcTrtrJmDOGWVaC3qQsvvn2UW9TnseNkmo7KBc3DJU7_NmlxWZArl1BdSetED0NCg - type: rouge value: 30.1706 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMzZGFjNzVkYWI0NTJkMmZjZDQ0YjhiYjIxN2VkNmJjMTgwZTk1NjFlOGU2NjNjM2VjYTNlYTBhNTQ5MGZkNSIsInZlcnNpb24iOjF9.xQ2LoI3PwlEiXo1OT2o4Pq9o2thYCd9lSCKCWlLmZdxI5GxdsjcASBKmHKopzUcwCGBPR7zF95MHSAPyszOODA - type: loss value: 2.7092134952545166 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzQzODE0NDc5YTYzYjJlMWU2YTVjOGRjN2JmYWVkOWNkNTRlMTZlOWIyN2NiODJkMDljMjI3YzZmYzM3N2JjYSIsInZlcnNpb24iOjF9.Vv_pdeFuRMoKK3cPr5P6n7D6_18ChJX-2qcT0y4is3XX3mS98fk3U1AYEuy9nBHOwYR3o0U8WBgQ-Ya_FqefBg - type: gen_len value: 15.1414 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjk5OTk3NWRiNjZlZmQzMmYwOTU2MmQwOWE1MDNlNTg3YWVkOTgwOTc2ZTQ0MTBiZjliOWMyZTYwMDI2MDUzYiIsInZlcnNpb24iOjF9.Zvj84JzIhM50rWTQ2GrEeOU7HrS8KsILH-8ApTcSWSI6kVnucY0MyW2ODxvRAa_zHeCygFW6Q13TFGrT5kLNAA --- ### 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). *Note: This model serves as a base version. For an even more advanced model with significantly enhanced performance, please check out our [advanced version](https://rapidapi.com/medoid-ai-medoid-ai-default/api/financial-summarization-advanced) on Rapid API. The advanced model offers more than a 16% increase in ROUGE scores (similarity to a human-generated summary) compared to our base model. Moreover, our advanced model also offers several convenient plans tailored to different use cases and workloads, ensuring a seamless experience for both personal and enterprise access.* ### 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/)