Online Transcript Summarizer Using Python

Abstract

This project proposes a novel approach to text summarization, depending on methods for machine learning (ML) and natural language processing (NLP), aimed at providing an efficient and accurate solution for summarizing text documents and articles. The proposed system is primarily intended for educational purposes and is available globally, catering to the growing demand for online educational content.

One of the significant challenges of extracting information from text documents is that it requires reading the entire article to understand the context, making it a time-consuming process. The proposed method uses Hugging Face Transformers and Pipelining to retrieve transcripts from the given article link and summarize it as per the required duration. The model accepts the article link and the desired summary duration as input and generates a summarized transcript as output.

According to the study's findings, the proposed method is quicker and more accurate than existing methods. The final text generated by the model accurately reflects the central concept of the article without any deviation. Additionally, the proposed system offers an efficient way to process large amounts of information quickly and accurately, making it a valuable tool for researchers, students, and educators.

The proposed text summarization system based on NLP and ML techniques offers a promising solution for processing large amounts of information quickly and accurately. It has the potential to significantly reduce the time required for summarizing text documents and articles, making it a valuable tool for students, researchers, and educators.