In our last AI project, we taught Python how to understand emotion. Now, let’s teach it how to read and summarize a long article for us using the Hugging Face Text Summarizer.
This is one of the most useful applications of modern Large Language Models (LLMs), and the transformers library makes it incredibly simple.
Step 1: Installation
pip install transformers
# You also need the 'sentencepiece' library for this specific model
pip install sentencepieceStep 2: The Code
We will use the same pipeline as before, but just tell it to do “summarization”.
from transformers import pipeline
# 1. Define a long piece of text to summarize
long_article = """
Python is an interpreted, high-level and general-purpose programming language.
Python's design philosophy emphasizes code readability with its notable use of
significant indentation. Its language constructs and object-oriented approach
aim to help programmers write clear, logical code for small and large-scale projects.
Python is dynamically-typed and garbage-collected. It supports multiple programming
paradigms, including structured (particularly, procedural), object-oriented,
and functional programming. Python is often described as a 'batteries included'
language due to its comprehensive standard library.
"""
# 2. Load the summarization pipeline
# This will download a model for you (e.g., 't5-base')
summarizer = pipeline("summarization")
# 3. Run the summarizer
# You can set min/max length for the summary
summary = summarizer(long_article, max_length=50, min_length=10, do_sample=False)
# 4. Print the result
print(summary[0]['summary_text'])Step 3: The Result
You will get a short, AI-generated summary like this: Python is an interpreted, high-level and general-purpose programming language. It supports multiple programming paradigms, including structured, object-oriented, and functional programming.
You just built a tool that can save you hours of reading!
