AI Academic Assistant for Summarization and Question Answering

The rapid expansion of academic literature has made efficient information extraction increasingly difficult for researchers, leading to substantial time spent manually summarizing documents and identifying key insights. This project presents an AI-powered Academic Assistant designed to streamline academic reading through multi-level summarization, contextual question answering, and source-grounded traceability. The system incorporates a robust preprocessing pipeline including text extraction, artifact removal, noise filtering, and section segmentation to prepare documents for accurate analysis. After assessing the limitations of traditional NLP and transformer-based summarization models, the project adopts a Large Language Model (LLM) approach using the Gemini API, enabling deeper semantic understanding, long-context processing, and flexible summarization. The assistant provides structured short, medium, and long summaries; contextual keyword extraction; and interactive question answering with transparent source highlighting. Limitations include handling complex visual content and occasional API constraints. Overall, this project demonstrates how modern LLMs, combined with tailored prompt engineering and structured preprocessing, can significantly enhance the academic document analysis workflow.