Research Paper Summarization Engine With Smart Text Chunking

Long documents break naive LLM summarization: a research paper of forty pages exceeds what a single prompt handles well, and crude truncation discards the methodology or the findings. The engineering answer is intelligent chunking, splitting text at meaningful boundaries, summarizing progressively, and merging results, and mastering it is a rite of passage for anyone building serious document AI. Researchers and students drowning in reading lists are the immediate beneficiaries. The intern builds a summarization engine that treats chunking as the core craft. Using Python with a Streamlit interface, the application accepts lengthy research papers and processes them through LangChain text splitters, with the intern comparing recursive character, token-aware, and section-respecting strategies for their effect on summary quality. OpenAI models summarize each chunk, and map-reduce and refine patterns combine the pieces into a coherent whole, with the intern implementing both and exposing a comparison so users can see how strategy changes output. Controls for summary length and focus complete the tool. The project demonstrates deep practical understanding of context-window engineering, including splitter selection, chain patterns for long-document processing, and empirical evaluation of summarization quality, skills that generalize to every document-heavy AI application.