About This Architecture

AI-powered extractive text summarization pipeline combining NLTK preprocessing, cosine similarity scoring, and NetworkX TextRank graph algorithms to automatically extract key sentences from documents. The architecture flows from user input through PDF extraction and text cleaning, then applies sentence similarity matrices and graph-based ranking to identify the most relevant content. This approach delivers fast, interpretable summaries without fine-tuned models, ideal for enterprises needing scalable document processing on OCI infrastructure. Fork this diagram to customize the NLP pipeline, swap ranking algorithms, or integrate additional LLM enhancement via Groq API for abstractive refinement. The modular design supports both direct text input and PDF uploads with downloadable summary outputs.

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How do you build an extractive text summarization pipeline on OCI using NLTK and TextRank?

This diagram shows a complete OCI NLP pipeline that ingests text or PDF documents, applies NLTK cleaning and tokenization, computes sentence similarity via cosine distance, constructs a TextRank graph using NetworkX, ranks sentences, and generates summaries with ROUGE evaluation. The Groq API module enables optional abstractive refinement, while Streamlit provides the user interface for input and