Text Summarization using N-gram Algorithm

Table of Contents

• Introduction
• Requirements
• Installation
• Algorithm
• Example

Introduction

This project implements a text summarization system using the N-gram algorithm. Text summarization is the process of generating a concise and coherent summary of a longer document or text. The N-gram algorithm is a statistical language model that identifies recurring sequences of N words in a given text and uses these sequences to generate summaries.

The goal of this project is to demonstrate how N-grams can be leveraged for text summarization and provide a simple and effective summarization tool for anyone working with textual data.

Requirements

To run this project, you will need the following:

• Python 3.x
• NumPy (for efficient array manipulation)
• PyPDF2

Installation

1. Install the required dependencies:

   pip install numpy
   

Algorithm

The N-gram algorithm works by tokenizing the input text into individual words and then extracting sequences of N words (N-grams). These N-grams are ranked based on their frequency in the text, and the most frequent ones are used to construct the summary. Shorter, more frequent N-grams are given higher importance.

The Relative Probability Formula for N-gram Algorithm:

Given a sequence of words or tokens, the relative probability of an n-gram (a sequence of n consecutive words or tokens) can be calculated as follows:

P(word_n | word_1, word_2, ..., word_(n-1)) = Count(word_1, word_2, ..., word_n) / Count(word_1, word_2, ..., word_(n-1))

Where:

• P(word_n | word_1, word_2, ..., word_(n-1)) is the relative probability of word_n given the context of the preceding n-1 words.
• Count(word_1, word_2, ..., word_n) is the number of occurrences of the n-gram (word_1, word_2, ..., word_n) in the training data.
• Count(word_1, word_2, ..., word_(n-1)) is the number of occurrences of the (n-1)-gram (word_1, word_2, ..., word_(n-1)) in the training data.

The n-gram algorithm is commonly used in natural language processing and statistical language modeling tasks to estimate the likelihood of word sequences based on their occurrences in the training data.

Example

Suppose we have the following input text:

"The quick brown fox jumps over the lazy dog. The dog barks at the moon. The fox and the dog become friends under the moonlight."

Using the N-gram algorithm with N=2, the summary might be:

"The quick brown fox jumps over the lazy dog. The dog barks at the moon."

NSE).