Introduction

With the explosion of digital information, extracting meaningful summaries from large volumes of text has become crucial for fast understanding and decision-making. Manual summarization is time-consuming and error-prone, especially when dealing with bulk data such as reports, articles, or documents.
The Raspberry Pi-based Intelligent Text Summarization System leverages the capabilities of natural language processing (NLP) to automate the summarization process. Implemented on a Raspberry Pi, this system can summarize input text files, documents, or even real-time transcribed speech into brief, informative content. Its low power requirement and portability make it ideal for educational, office, and field use.

Working Principle

The system uses Natural Language Processing (NLP) algorithms to extract the most important sentences or phrases from a given text. The Raspberry Pi acts as the processing unit running summarization algorithms such as TextRank, TF-IDF, or BERT-based models. Input text is pre-processed (tokenized, filtered, and cleaned), and then passed through a summarization model. The output is a condensed version of the input text that retains the core information.

Methodology

1. Text Input: Accepts input in the form of .txt files or real-time transcribed speech.

2. Text Preprocessing:

   • Tokenization (breaking down text into words/sentences)

   • Stop-word removal and stemming

3. Summarization Algorithm Execution:

   • Rule-based (TextRank or TF-IDF) or

   • ML-based (e.g., BERT, if lightweight and compatible)

4. Result Generation:

   • The Raspberry Pi generates a summarized version and displays or stores it.

   • Optional audio/text output for accessibility.

5. Display and Storage:

   • Summary is shown on a screen (LCD/console) and saved as a file for later use.

Components Used