Summary of Can We Trust the Performance Evaluation Of Uncertainty Estimation Methods in Text Summarization?, by Jianfeng He et al.
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?
by Jianfeng He, Runing Yang, Linlin Yu, Changbin Li, Ruoxi Jia, Feng Chen, Ming Jin, Chang-Tien Lu
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive benchmark is introduced to evaluate uncertainty estimation on text summarization (UE-TS) evaluation methods, addressing concerns about inaccurate summaries in risk-critical applications. The benchmark incorporates 31 NLG metrics across four dimensions and evaluates the uncertainty estimation capabilities of three large language models and one pre-trained language model on three datasets. The study emphasizes the importance of considering multiple uncorrelated NLG metrics and diverse uncertainty estimation methods to ensure reliable evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text summarization is a crucial task in natural language generation, but it’s essential to ensure accuracy in risk-critical applications where humans make decisions based on summaries. The problem lies in estimating uncertainty when evaluating text summarization. To solve this issue, researchers created a benchmark with 31 metrics across four dimensions. They tested three large language models and one pre-trained model on three datasets. The study found that using multiple uncorrelated metrics and different methods to estimate uncertainty is crucial for reliable evaluation. |
Keywords
» Artificial intelligence » Language model » Summarization
