Smarter Summaries with Agentic AI: The Future of Summary Evaluation

This article is originally published on https://capestart.com/technology-blog/agentic-ai-driven-insight-the-new-frontier-of-summary-evaluation/

Introduction: Achieving Accurate Summarization

Imagine your AI assistant confidently summarizing a 50-page clinical trial report, but including a fabricated statistic that could influence a million-dollar drug approval decision. How would you know?

AI Summary Using a Typical Setup

Typical AI Agent Generating Summary

Traditional metrics like ROUGE focus on word overlap but fail to answer these critical questions: Is the summary accurate? Does it make sense? Does it truly capture the essence of the content? ROUGE, while useful for measuring lexical similarity, cannot discern factual correctness, logical flow, or the depth of information conveyed.

To address these gaps, we explore advanced LLM-based evaluation frameworks and, eventually, Agentic AI Systems. These new approaches aim to deeply understand content, ensuring AI-generated summaries are trustworthy and meaningful.

Beyond Simple Word Matching: What Makes a Good Summary?

Traditional metrics are like trying to judge a painting by counting the colors. They miss the artistry. Here’s what makes a summary great:

Accuracy: The summary must be factually correct. It should not contain any information that contradicts the source, nor should it invent details that aren’t present.

Clarity: The summary needs to make logical sense as a standalone piece of text. It should flow well, with ideas connected in a way that is easy for a reader to understand.

Completeness (Essence): It should distill the most important information and key findings from the original document, effectively conveying its core message without unnecessary detail. It’s about capturing the main points and overall meaning.

The Human Touch: A Framework for Quality Evaluation

To assess summary quality comprehensively, consider this evaluation framework:

Rate each criterion (Consistency, Relevance, etc.) on a scale of 1 (Poor) to 5 (Excellent).
Use the “Key Elements to Check” for each criterion (see table below).
Calculate an overall score (sum of individual scores).

Detailed Evaluation Criteria

The following table provides detailed information on the evaluation criteria to create a summary that is accurate, relevant and coherent.

Rating Scale

The suggested Rating Scale for the overall score (based on estimated human rework required) is:

26-30: Minimal Rework Needed: The summary is of excellent quality and requires very little to no human intervention.
21-25: Light Rework Needed: The summary is good, but minor edits or refinements may be necessary.
16-20: Moderate Rework Needed: The summary is fair but requires significant editing and improvement.
11-15: Heavy Rework Needed: The summary is poor and requires substantial restructuring and rewriting.
6-10: Complete Rewrite Needed: The summary is unacceptable and requires a complete overhaul.

Agentic AI: Automating the Summary Evaluation

While human evaluation is valuable, it’s also time-consuming and resource-intensive. Agentic AI Systems offer a potential solution: automating the evaluation process with multiple AI agents working together.

How it works:

Inputs: Source text, summary, and (optionally) human-written reference summaries, defined quality elements.
Evaluation Agents: Each agent focuses on a specific evaluation criterion (Consistency, Relevance, etc.).
Scoring Agent: Aggregates the individual scores and justifies an overall rating.

Building an Agentic Evaluation System: The Architecture

Structure of an Agentic AI System for Summary Evaluation

Agent Configuration and Template

This document details the setup of an autonomous agent system focused on thoroughly assessing the factual accuracy of AI-generated summaries compared to their source materials. Each agent in this system is given a unique role, purpose, operational background (context), specific assignment, and a set output structure. For example, specific assessment standards like “consistency” are integrated directly into the agent’s instructions.

• Role: Consistency Expert.
• Goal: Evaluate the summary for factual consistency with the source document.
• Backstory: You are a seasoned fact-checker with expertise in identifying inconsistencies and inaccuracies.
• Task:
  • Read the source document and the generated summary.
  • Identify any factual discrepancies between the summary and the source.
  • Assign a score (1-5) for consistency, with 5 being fully consistent and 1 being highly inconsistent.
  • Provide a detailed justification for the score, highlighting any specific inconsistencies found.
• Expected Output:
  • Score: (1-5)
  • Justification: “…”

Tools and Frameworks

Suggested Tools and Technologies for building such systems include the following.

• Agentic AI Frameworks: Autogen, CrewAI, OpenAI Swarm, LangGraph.
• LLM APIs: OpenAI (GPT-3.5, GPT-4, vision models), Anthropic (Claude), Google (Gemini), Perplexity.
• Open-Source LLMs: Fine-tuned versions of models like Llama, Falcon, or Mistral can be hosted and used as endpoints.
• Reasoning Models: OpenAI’s reasoning models (o1, o2, o3) and Multi-modal models like GPT-Omni, LLama, Anthropic Sonnet.

Addressing the Challenges of LLM Evaluation

LLM-based evaluation isn’t without its hurdles. We need to be aware of potential pitfalls and implement strategies to mitigate them:

While LLM-based evaluation, particularly through agentic systems, offers a significant leap forward, it requires vigilance against inherent limitations. Evaluator LLMs themselves can hallucinate, produce inconsistent results due to their stochastic nature, and exhibit biases learned from their training data. Mitigating these issues involves specialized fine-tuning with domain-specific examples, grounding evaluations with source-document evidence (e.g., RAG), employing deterministic sampling techniques for reproducibility, developing unambiguous prompts and rubrics, and actively auditing for and addressing model biases.

Furthermore, the practical deployment of these systems must consider the computational cost and latency of sophisticated evaluations, alongside the inherent difficulty in objectively measuring subjective qualities like “clarity” or “capturing the essence.” Strategies here include tiered evaluation systems using models of varying complexity, optimizing resource usage, decomposing subjective criteria into more measurable components, and leveraging human feedback to train models on nuanced assessments. Ultimately, building trustworthy LLM-driven evaluation necessitates a continuous cycle of development, rigorous testing, and crucial human oversight to validate and refine the automated assessments.

The Real-World Impact: How Summarization is Reshaping Industries, Especially in Life Sciences

LLM-powered summarization isn’t just about saving time; it’s fundamentally changing workflows and driving innovation across industries, with a particularly profound impact on the life sciences.

• Rapid Literature Review: Quickly synthesize scientific literature from research papers, clinical trial reports, and patents to identify key findings, trends, and drug targets, accelerating drug discovery and development.
• Streamlined Regulatory Submissions: Expedite the creation of critical regulatory documents (e.g., CTDs) by automatically summarizing preclinical and clinical data, ensuring consistency and completeness.
• Improved Market Access Strategies: Develop concise, evidence-based Global Value Dossiers (GVDs) by condensing complex clinical trial and health economic data, supporting market access and reimbursement decisions.
• Enhanced Pharmacovigilance: Analyze large volumes of adverse event reports and patient feedback through automated summaries, enabling faster safety signal detection and risk mitigation.
• Optimized Medical Affairs Communication: Equip Medical Science Liaisons (MSLs) with concise summaries of publications and guidelines, fostering informed discussions and improving patient care.

This transformation is also giving rise to new roles:

• Prompt Engineers: Crafting effective prompts to guide LLMs.
• Finetuning Specialists: Optimizing LLM accuracy and performance, particularly for specialized life science datasets.
• Agentic AI System Architects: Designing and implementing complex multi-agent systems.
• Evaluation and Validation Engineers: Ensuring the quality and reliability of LLM-generated summaries. Specifically, it involves validating summaries against established scientific and regulatory standards.

Conclusion: Embracing the Future of Information Processing

LLM-powered summarization is transforming the way we process information. Advanced LLMs, together with robust evaluation frameworks and Agentic AI systems, improve efficiency, innovation, and decision-making. While challenges remain, the partnership between humans and AI is key to unlocking the full potential of this technology. LLM-based summarization represents a critical step forward, empowering us to focus on higher-value tasks and drive progress across all fields. The future isn’t just AI-generated. It’s AI-evaluated, AI-improved, and human-validated.