Content Summarization and Topic Modeling System

Geek idea:

• Automated Article Content Summarization & Topic Identification System

Overview: The idea here is to create an NLP-based tool that reads a published article and automatically identifies the key topics, themes, and a succinct summary of what the article is about based on its content, title, keywords or tags. It could handle academic papers, news articles, or even blog posts. To achive this, we might use the following python peatures: Topic Modeling, Text Summarization, and Named Entity Recognition (NER). The system would be able to extract key topics and provide a concise summary of the article to give readers a quick understanding of the article’s core subject.

Why it’s a geeky idea:

• Advanced NLP Techniques: You’ll be working with techniques like Topic Modeling (e.g., LDA or BERTopic), Text Summarization (e.g., extractive or abstractive methods), and NER to automatically extract the essence of a text.
• Real-World Application: This could be super useful for academic researchers, news aggregators, or even social media platforms where users often want a quick idea of what an article is about before deciding whether to engage with it.
• Complexity: The system will need to handle nuances in language, extract contextual meaning, and be able to distinguish between various levels of detail (e.g., identifying the difference between an academic article, news piece, or opinion article).