Program 7
Aim:
Summarize
long texts using a pre-trained summarization model using Hugging face model.
Load the summarization pipeline. Take a passage as input and obtain the
summarized text.
Theory:
Text
Summarization using a Pre-trained Hugging Face Model
Text
summarization is a Natural Language Processing (NLP) technique used to convert
a long piece of text into a shorter version while keeping the important
information. It helps users quickly understand the main idea of large
documents.
In
real-world applications, text summarization is used for news articles, research
papers, reports, emails, and meeting transcripts. It saves time by providing a
concise summary instead of reading the entire document.
Hugging
Face Transformers provides many pre-trained models that can perform automatic
text summarization. These models are trained on large datasets and learn how to
identify the most important information in a passage.
The
summarization pipeline in Hugging Face makes it easy to generate summaries with
just a few lines of code. By loading the summarization pipeline, we can give a
long passage as input, and the model will generate a short summarized version
of the text.
Thus,
using a pre-trained Hugging Face model allows efficient and automatic
summarization of large texts, making it useful in many real-world NLP
applications.
Program:
Step 1: Install Correct Version
Step
2: Restart Runtime
Click
Runtime → Restart Runtime
Step
3: Import Pipeline
Step
4: Load Summarization Model
Step
5: Input Passage
Step
6: Generate Summary
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