Abstractive summarization in NLP

Introduction

Abstractive summarization helps create short, clear summaries by understanding and rewriting the main ideas in your own words.

You want a quick summary of a long news article without reading everything.

You need a brief overview of a research paper for a presentation.

You want to generate a summary of customer reviews to understand common opinions.

You want to create a short description of a long email or report.

You want to help people with reading difficulties by providing simpler summaries.

Syntax

NLP

from transformers import pipeline

summarizer = pipeline('summarization')

summary = summarizer(text, max_length=100, min_length=30, do_sample=False)
print(summary[0]['summary_text'])

The pipeline function loads a ready-to-use summarization model.

max_length and min_length control the summary size.

Examples

Generates a short summary between 20 and 50 tokens.

NLP

summary = summarizer(long_text, max_length=50, min_length=20, do_sample=False)
print(summary[0]['summary_text'])

Generates a longer summary with some randomness for variety.

NLP

summary = summarizer(long_text, max_length=150, min_length=80, do_sample=True)
print(summary[0]['summary_text'])

Handles short input text gracefully by summarizing as much as possible.

NLP

summary = summarizer('Short text', max_length=30, min_length=10, do_sample=False)
print(summary[0]['summary_text'])

Sample Model

This program loads a ready-to-use summarization model, summarizes a short paragraph about machine learning, and prints both the original text and the summary.

NLP

from transformers import pipeline

# Load the summarization pipeline
summarizer = pipeline('summarization')

# Example long text
text = ("Machine learning is a method of data analysis that automates analytical model building. "
        "It is a branch of artificial intelligence based on the idea that systems can learn from data, "
        "identify patterns and make decisions with minimal human intervention.")

print("Original text:")
print(text)

# Generate summary
summary = summarizer(text, max_length=50, min_length=20, do_sample=False)

print("\nSummary:")
print(summary[0]['summary_text'])

Important Notes

Abstractive summarization models usually use deep learning and large pre-trained models.

It can sometimes create new phrases not in the original text, unlike extractive summarization.

Running these models requires good hardware or cloud services due to their size.

Summary

Abstractive summarization rewrites main ideas in short form.

It uses AI models that understand and generate new sentences.

Useful for quickly grasping long texts in many real-life situations.