File size: 8,911 Bytes
120d484 5a1b512 4c7b827 120d484 e3b7f9d 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 a720f82 5a1b512 120d484 3af34e3 0f25402 3af34e3 0f25402 2ff42a7 0f25402 734fe2d b5740b7 3af34e3 0f25402 e8e3a2f 0f25402 b8d46cf 0f25402 32d7ccb 33b68fc 32d7ccb 33b68fc 32d7ccb 33b68fc bb24c62 32d7ccb 3af34e3 56a83e3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
---
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/)
|