FineSurE: Fine-grained Summarization Evaluation using LLMs
FineSurE uses LLMs for fine-grained summarization evaluation, improving completeness and conciseness.
Key Findings
Methodology
FineSurE is a fine-grained summarization evaluation framework using large language models. It evaluates summaries based on faithfulness, completeness, and conciseness through fact-checking and keyfact alignment, utilizing both open-source and proprietary LLMs.
Key Results
- FineSurE achieved 86.4% accuracy in sentence-level faithfulness evaluation on the FRANK dataset, significantly outperforming existing methods.
- On the REALSumm dataset, FineSurE showed higher correlation with human evaluations in completeness and conciseness than other evaluators.
- FineSurE can identify error types for each sentence and provide detailed alignment information at the keyfact level.
Significance
FineSurE holds significant value in academia and industry by addressing the limitations of traditional methods in fine-grained evaluation, offering a more accurate assessment of summary quality. It enhances the evaluation process, facilitating faster development and optimization of text summarization models.
Technical Contribution
FineSurE fundamentally differs from existing SOTA methods by its fine-grained evaluation capability. It introduces new evaluation dimensions like completeness and conciseness, providing detailed error classification and alignment information.
Novelty
FineSurE is the first framework to use LLMs for fine-grained summarization evaluation. It offers significant innovations in evaluation dimensions and detail compared to existing methods.
Limitations
- FineSurE may encounter context truncation issues with long text inputs, affecting evaluation accuracy.
- Automatically generated keyfacts may lack accuracy without human input.
Future Work
Future work could include improving the accuracy of automatic keyfact generation and extending FineSurE to support more types of text generation tasks.
AI Executive Summary
Automated evaluation of text summarization is a critical task in natural language processing. Traditional methods like ROUGE show weak correlation with human judgment, especially in fine-grained evaluation. Recent LLM-based methods are limited to summary-level assessments, lacking detailed analysis.
FineSurE is an innovative fine-grained evaluation framework specifically designed for summarization tasks. It evaluates summaries based on faithfulness, completeness, and conciseness through fact-checking and keyfact alignment. This multi-dimensional approach provides a more accurate reflection of summary quality.
Experimental results demonstrate FineSurE's superior performance across multiple datasets, particularly in completeness and conciseness. It not only improves evaluation accuracy but also offers deeper analytical tools for model development. However, FineSurE faces challenges with long text inputs, which is a direction for future research.
Deep Analysis
Background
Text summarization is a key task in natural language processing, aiming to generate concise summaries containing the main information of the original text. Traditional evaluation methods like ROUGE, though widely used, show weak correlation with human judgment, especially in fine-grained evaluation. Recently, LLM-based evaluation methods have emerged, but they typically provide only summary-level assessments, lacking detailed analysis of specific errors and information omissions.
Core Problem
Existing automated evaluation methods struggle to match the granularity and accuracy of human evaluations. Particularly in assessing the faithfulness, completeness, and conciseness of summaries, traditional methods fail to provide detailed error classification and information alignment analysis. This limits developers' understanding and improvement of generated summary quality.
Innovation
FineSurE addresses the limitations of existing methods by introducing a fine-grained evaluation framework. Its innovations include using large language models for fact-checking and keyfact alignment, providing detailed evaluations across faithfulness, completeness, and conciseness. This approach not only improves evaluation accuracy but also offers richer information for model development.
Methodology
- �� Use large language models for fact-checking, identifying specific error types in each sentence.
- �� Conduct keyfact alignment to ensure summaries cover all important information.
- �� Calculate percentage scores for faithfulness, completeness, and conciseness, providing detailed error classification and alignment information.
Experiments
Experiments were conducted on the FRANK and REALSumm datasets, using various open-source and proprietary LLMs as the evaluation framework's backbone. Evaluation metrics included sentence-level accuracy and correlation with human evaluations. Results showed FineSurE's superior performance across multiple dimensions.
Results
FineSurE achieved 86.4% sentence-level accuracy on the FRANK dataset and higher correlation with human evaluations in completeness and conciseness on the REALSumm dataset. It also demonstrated the ability to identify specific error types for each sentence and provide detailed alignment information at the keyfact level.
Applications
FineSurE can be used for the development and optimization of text summarization models, particularly in scenarios requiring fine-grained evaluation. It provides developers with deeper analytical tools to improve the quality of generated summaries.
Limitations & Outlook
FineSurE may encounter context truncation issues with long text inputs, affecting evaluation accuracy. Additionally, automatically generated keyfacts may lack accuracy without human input, which is a challenge for future research.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. FineSurE is like a chef's assistant, helping you check if each ingredient is accurate (faithfulness), ensuring all important ingredients are used (completeness), and avoiding unnecessary spices (conciseness). This way, FineSurE helps you create the perfect dish, just like it helps generate high-quality text summaries.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game where you need to summarize a story. FineSurE is like your game buddy, helping you check if each story piece is correct (like checking game tasks), making sure you don't miss important parts (like completing all tasks), and not adding extra stuff (like not wasting time). This way, you can easily win the game!
Glossary
ROUGE
A metric for evaluating text summaries by measuring n-gram overlap between generated and reference summaries.
Used to compare FineSurE with traditional evaluation methods.
LLM (Large Language Model)
A deep learning-based language model capable of generating and understanding natural language text.
FineSurE uses LLMs for fine-grained evaluation.
Faithfulness
An evaluation metric assessing whether a generated summary accurately reflects the source text information.
FineSurE evaluates faithfulness through fact-checking.
Completeness
An evaluation metric assessing whether a generated summary includes all important information.
FineSurE evaluates completeness through keyfact alignment.
Conciseness
An evaluation metric assessing whether a generated summary avoids unnecessary information.
FineSurE evaluates conciseness by calculating the density of relevant sentences.
Open Questions Unanswered questions from this research
- 1 How can the accuracy of automatically generated keyfacts be improved without human input?
- 2 How can FineSurE address context truncation issues when handling long text inputs?
Applications
Immediate Applications
Text Summarization Optimization
FineSurE can be used to optimize text summarization models, helping developers improve the quality of generated summaries.
Long-term Vision
Multi-task Text Generation
FineSurE's fine-grained evaluation framework can be extended to other text generation tasks, such as machine translation and text generation.
Abstract
Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional methods like ROUGE do not correlate well with human judgment, while recently proposed LLM-based metrics provide only summary-level assessment using Likert-scale scores. This limits deeper model analysis, e.g., we can only assign one hallucination score at the summary level, while at the sentence level, we can count sentences containing hallucinations. To remedy those limitations, we propose FineSurE, a fine-grained evaluator specifically tailored for the summarization task using large language models (LLMs). It also employs completeness and conciseness criteria, in addition to faithfulness, enabling multi-dimensional assessment. We compare various open-source and proprietary LLMs as backbones for FineSurE. In addition, we conduct extensive benchmarking of FineSurE against SOTA methods including NLI-, QA-, and LLM-based methods, showing improved performance especially on the completeness and conciseness dimensions. The code is available at https://github.com/DISL-Lab/FineSurE-ACL24.