/TextSummariszer

The project adopts a modular approach to achieve multilingual text summarization. It starts with user-provided input, supporting multiple languages such as English, Hindi, and Bengali. Language detection helps identify the input language for further processing. We utilize pre-trained transformer models, such as BART and T5, for text summarization.

Primary LanguageJupyter Notebook

In today's fast-paced digital landscape, dealing with the vast amount of information we encounter daily can be overwhelming. Extracting the most crucial insights from lengthy documents or articles is an increasingly vital skill. Introducing our "Text Summarization Tool," a solution designed to make this process swift and effortless.

Our tool provides a user-friendly platform that caters to a diverse range of users. Whether you're a student preparing for an exam, a professional researching a topic, or a casual reader trying to keep up with the latest news, the Text Summarization Tool simplifies the task of creating concise and informative summaries. It's your companion for efficient information consumption in a world where time is precious.

One of the standout features of our Text Summarization Tool is its ability to handle multiple languages. It can accept text in various languages and provide summaries in your preferred language. Language barriers become a thing of the past, making it accessible to a global audience. In an age where access to information knows no borders, this tool bridges the gap and empowers users worldwide to quickly grasp the content that matters most.

The primary objective of this research project is to design, develop, and implement a specialized Natural Language Processing (NLP) model tailored to develop a text summarization tool.

  1. Accepts textual content in multiple languages, specifically English, Hindi, and Bengali.
  2. Detects the language of the input text.
  3. Translates the text to English if it's not already in English.
  4. Summarizes the text in English using a state-of-the-art summarization model.
  5. Optionally translates the summary back to the original language based on user preference.
  6. Ensures that the generated summary is concise, coherent, and retains the key information from the original text.

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