Automated News Digest Generator
Developed a Python application that automates the aggregation and summarization of news articles from various RSS feeds. The application reads multiple news sources, extracts key information using OpenAI's language model, GPT-3.5 Turbo, and generates concise summaries.
It is able to send a digested news summary from rss feeds every day.
Each digest is then formatted into an email newsletter layout, complete with article titles, links for further reading, and summaries.
The system is designed with modularity in mind, allowing for easy expansion or modification, integrating smoothly with email services for timely delivery.
API integration
Data parsing
Automated content generation
Python and Linux programming
Website Creation
Subscriber management
User Interest Profiling and Personalization: Implement a user profiling system where users can specify their interests or select from predefined categories (e.g., technology, sports, health). Use this information to curate and highlight articles that align with each user's interest profile, ensuring they receive a tailored news summary.
Interactive Email Content: Create interactive elements within the email, such as collapsible sections for each news category, allowing users to expand or collapse sections based on what they want to read. This could also include interactive polls or feedback sections to engage users and learn more about their preferences.
Natural Language Processing (NLP) for Better Summarization: Integrate advanced NLP techniques to improve the quality of summaries. This could involve using algorithms to identify the most relevant sentences or employing named entity recognition to ensure that key people, places, and organizations are included in the summary.
Description
Skills Learned and Applied
Future Enhancement
The website is down at the moment