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Conciseness

Conciseness measures how efficiently a summary conveys information without unnecessary words or phrases. It evaluates the summary's ability to communicate key points clearly and briefly while maintaining essential information.

Overview​

The conciseness metric uses an LLM provider to analyze how well a summary balances information completeness with brevity. It helps identify whether a summary is appropriately succinct while retaining crucial content from the source text.

Usage​

Here's how to evaluate conciseness using Assert LLM Tools:

from assert_llm_tools.core import evaluate_summary
from assert_llm_tools.llm.config import LLMConfig


metrics = ["conciseness"]
# Configure LLM provider (choose one)
llm_config = LLMConfig(
    provider="bedrock",
    model_id="anthropic.claude-v2",
    region="us-east-1"
)

llm_config = LLMConfig(
    provider="openai",
    model_id="gpt-4-mini",
    api_key="your-api-key"
)

# Example texts
full_text = "The new energy policy aims to reduce carbon emissions by 30% by 2030 through implementing renewable energy solutions."
summary = "Policy targets 30% carbon reduction by 2030 via renewables."

# Evaluate conciseness
metrics = evaluate_summary(
    full_text,
    summary, 
    metrics=metrics,
    llm_config=llm_config
)

# Print results
print("\nEvaluation Metrics:")
for metric, score in metrics.items():
    print(f"{metric}: {score:.4f}")

Interpretation​

The conciseness score ranges from 0 to 1:

  • 1.0: Optimal conciseness (efficient information delivery)
  • 0.0: Poor conciseness (verbose or inefficient communication)

When to Use​

Use conciseness metrics when:

  • Optimizing summary length
  • Evaluating text compression quality
  • Training summarization models
  • Generating executive summaries or abstracts
  • Creating content for space-constrained formats

Limitations​

  • Requires an LLM provider, which may incur costs
  • May not account for domain-specific verbosity requirements
  • Balance between detail and brevity can be subjective
  • Results may vary depending on the LLM model used