financial summarization pegasus

Name: financial summarization pegasus
Author: human-centered-summarization

Providerhuman-centered-summarization

Categorysummarization

LicenseApache-2.0

Downloads154.5K

Stars0

Overview

Financial Summarization Pegasus is a domain-specific encoder-decoder model fine-tuned for the nuances of financial reporting. Unlike general-purpose summarizers, this model is optimized to handle the dense terminology and structural complexity of earnings calls, annual reports, and market analysis. For developers, this means higher factual density and a reduced risk of 'hallucinations' when processing quantitative data. It integrates easily into data pipelines via standard Hugging Face transformers, making it an ideal choice for building automated financial dashboards, news aggregators, or internal risk assessment tools where precision outweighs creative prose.

Highlights

Optimized for dense financial terminology and reporting structures
Reduces hallucinations in quantitative data summaries
Seamless integration via Apache-2.0 licensed framework
Ideal for automated market analysis and reporting pipelines

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("human-centered-summarization/financial-summarization-pegasus")
tokenizer = AutoTokenizer.from_pretrained("human-centered-summarization/financial-summarization-pegasus")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download human-centered-summarization/financial-summarization-pegasus

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download human-centered-summarization/financial-summarization-pegasus config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('human-centered-summarization/financial-summarization-pegasus')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/human-centered-summarization/financial-summarization-pegasus

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/human-centered-summarization/financial-summarization-pegasus

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('human-centered-summarization/financial-summarization-pegasus')
tokenizer = AutoTokenizer.from_pretrained('human-centered-summarization/financial-summarization-pegasus')

Full Documentation

来源: HuggingFace

---
language:

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, on topics such as stock, markets, currencies, rate and cryptocurrencies.

It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. 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.

*Note: This model serves as a base version. For an even more advanced model with significantly enhanced performance, please check out our advanced version 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 S

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