Document-Based Text Summarization using T5 small and gTTS

The research project on “Deep Learning-Based Text Summarization System using T5 small and gTTS” introduces a method to automatically extract and understand information from PDFs. The first step is to extract text from PDF files accurately. Following this, advanced natural language processing techniques are applied, utilizing a BERT-based model for sentiment analysis to identify emotional nuances in the text. Additionally, the integration of the T5 model streamlines the text summarization process, condensing extensive information into a clear and concise summary. The sophistication of the project is enhanced through the inclusion of Google Text-to-Speech (GTTS) capabilities, converting the written summary into an audio file. This feature accommodates various user preferences, improving overall accessibility. The research establishes a multimodal strategy for information dissemination, delivering the summary in both written and audio formats for a concise yet inclusive presentation. Beyond its technical contributions, the document summarization system has applications in education, content curation, and information retrieval. This system can assist educators in creating concise educational materials, support content curators in efficiently summarizing articles, and aid users in quickly extracting relevant information from large datasets, showcasing its versatility and potential impact across different fields. The integration of advanced natural language processing techniques underscores their adaptability and efficiency in handling textual data, ultimately enhancing the overall user experience and accommodating a broader spectrum of use.