Optimized Semantic Abstraction for HighPrecision Text Summarization in NLP Systems

The project offers a text summarization tool that can handle content from PDFs, DOCX, and plain text. It incorporates extractive and abstractive summarization methods for creating abridged summaries. The extractive method applies sentence tokenization in the selection of important sentences, whereas the abstractive method utilizes a pre-trained Hugging Face model. The tool also has a Streamlit-based user interface for handling multiple file uploads and direct input of text. Strong error handling and text cleansing improve dependability and summarization accuracy, so the tool is useful for applications needing effective text analysis like in healthcare, research, and legal arenas.