Summary of Evaluate Summarization in Fine-granularity: Auto Evaluation with Llm, by Dong Yuan et al.
Evaluate Summarization in Fine-Granularity: Auto Evaluation with LLM
by Dong Yuan, Eti Rastogi, Fen Zhao, Sagar Goyal, Gautam Naik, Sree Prasanna Rajagopal
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the challenge of accurately evaluating summarization models, particularly when dealing with long and unstructured texts. Existing methods like ROUGE and embedding similarities often yield scores that have low correlation with human judgments and are not easily interpretable. LLMs can mimic human reviews but subjective scores are difficult to justify. To address these challenges, the authors introduce SumAutoEval, a novel evaluation methodology and tooling that provides a more comprehensive, accurate, and interpretable assessment of summarization outputs. The method evaluates metrics at varying granularity levels on four key dimensions: completeness, correctness, alignment, and readability. Experimental results demonstrate that SumAutoEval improves human correlation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Summarization is important for processing huge amounts of information efficiently. But it’s hard to decide if a summary is good or not. Some methods don’t match how humans think about summaries. This paper proposes a new way to evaluate summarizations, called SumAutoEval. It looks at summaries from different angles and gives scores based on completeness, correctness, alignment, and readability. The results show that this method helps understand summary quality better. |
Keywords
» Artificial intelligence » Alignment » Embedding » Rouge » Summarization
