semantic_similarity

from sentence_transformers import SentenceTransformer, util

# Load a pre-trained sentence transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

def semantic_similarity(generated, reference):
    """
    Computes the cosine similarity between the embeddings of the generated summary and a reference summary.
    """
    embeddings = model.encode([generated, reference])
    similarity = util.cos_sim(embeddings[0], embeddings[1])
    return similarity.item()

# Example usage
reference_summary = (
    "Climate change is a major challenge marked by rising temperatures and extreme weather. "
    "Human actions like deforestation and pollution worsen the problem, and scientists urge immediate action to cut emissions."
)
generated_summary = (
    "Climate change is a major contemporary challenge, characterized by rising global temperatures that cause extreme weather, "
    "melting ice, and ecosystem disruptions. Human activities like deforestation and industrial pollution exacerbate these effects. "
    "Scientists stress the urgency of reducing greenhouse gas emissions to mitigate environmental impacts."
)

sim_score = semantic_similarity(generated_summary, reference_summary)
print(f"Semantic Similarity Score: {sim_score:.2f}")