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from transformers import pipeline
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summarizer = pipeline("summarization", model="facebook/bart-large-xsum")
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def summarize(text, max_length=150, min_length=30):
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"""Summarize the input text using a pre-trained model."""
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summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)
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return summary[0]['summary_text']
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if __name__ == "__main__":
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sample_text = (
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"The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. "
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"It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
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"Constructed from 1887 to 1889 as the entrance to the 1889 World's Fair, it was initially "
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"criticized by some of France's leading artists and intellectuals for its design, but it "
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"has become a global cultural icon of France and one of the most recognizable structures in the world."
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)
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print("Original Text:\n", sample_text)
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print("\nSummary:\n", summarize(sample_text)) |
