Provide a framework for implementing financial news summarization and sentiment analysis using generative AI. The result shall be to generate concise, accurate, and informative summaries of financial news articles.
The step-by-step framework has two essential and distinguished phases:
1.)First step phase emphasizes the elaboration of the summarization feature and the capability to summarize financial-related text and content both from selected web pages and from financial news-related API-es using pre-trained language models like T5 and BERT.
2.)Second step phase enhances the sentiment analysis for each summarization excited and outlines the sentiment eather is positive, negative or neutral.
The objective is to build a financial news summarization and sentiment analysis pipeline, step-by-step starting with the 1.)Firts step phase and then following the 2.)Second step phase. The result shall be to generate concise, accurate, and informative summaries of financial news articles.

The summaries 1.)First step should:
accurately reflect the main points and essential details of the original article,
be written in clear and fluent language, easy for a non-expert to understand,
condense the information while preserving the overall meaning and context,
focus on information that is relevant to financial analysis and decision-making (e.g., company performance, market trends, regulatory changes),
if the original article is positive, negative, or neutral in tone, the summary should reflect this
using pre-trained language models like T5 and BERT

For the 1.First step for summarizing task use tools for financial Web scraping of pages :
Blumberg
Reuters
Financial times
Finance.si
with tools and Python libraries like this one:
https://pypi.org/project/beautifulsoup4/
https://scrapy.org/
using pre-trained language models like T5 and BERT.

https://huggingface.co/ProsusAI/finbert
https://github.com/ProsusAI/finBERT

News
https://newsapi.org/
https://newsapi.ai/intro-python
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The result shall be an accurate classification of the sentiment of financial news articles and their summaries.

The sentiment analysis 2.)Second step should:
reliably classify the sentiment of the text as positive, negative, or neutral,
distinguish between different degrees of sentiment (e.g., strongly positive, mildly negative),
correctly interpret potentially ambiguous language (e.g., "volatility" can be positive or negative depending on the investor's perspective).

For the 2.)second step analyzing task and understanding the structure and content of a financial news dataset prepare the data for AI models as follows:
Data tool & library: Pandas, Numpy, Matplotlib, Seaborn, Pydantic, Google BERT Bidirectional Encoder Representations from Transformers, T5 HugginfFace text-to-text transformer.
Text Cleaning:
Remove special characters, HTML tags, and stop words.
Normalize text (e.g., lowercasing, stemming).
Tokenization:
Split text into sentences or words.
Metadata Extraction:
Extract publication date, source, and tags for context.

For the Data Collection use tools like Beautiful Soup, Scrapy, NewsAPI

Additionally, Modernize the Data Visualization using the Python code script Grafana, and for the evaluation metrics of the pipeline model and the deliverables use ROUGE and F-1 score.
https://github.com/grafana/grafana
https://play.grafana.org/

Follow this next step-by-step instruction, and wait for the prompt trigger before starting with the 1.)First step phase and also wait for the prompt trigger to proceed and continue with the 2.)Second step phase. Start with the workflow pipeline description elaborating the orchestration for the objective.
Then, compile comprehensive deliverables as documentation that explains methodologies for each pipeline stage and provides insights on model, libraries, and tool selection.
After that, showcase the codebase architecture, functional scripts, and files for the modular structure: Preprocessing, Summarization, Sentiment analysis, Evaluation, and Visualization of pipeline stages, results, and challenges.
For the evaluation metrics of the pipeline model and the deliverables use ROUGE and F-1 score.
Do it step-by-step, each phase separately!