Summary of Unisumeval: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation For Llms, by Yuho Lee and Taewon Yun and Jason Cai and Hang Su and Hwanjun Song
UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMs
by Yuho Lee, Taewon Yun, Jason Cai, Hang Su, Hwanjun Song
First submitted to arxiv on: 30 Sep 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 A novel benchmark for summarization quality evaluation, called UniSumEval, is proposed to address limitations in existing benchmarks. The new benchmark extends the range of input context and provides fine-grained, multi-dimensional annotations, created with AI assistance to identify potentially hallucinogenic texts and aid human annotators. Nine latest language models are benchmarked as summarizers across varying input contexts and evaluation dimensions, offering insights into their performance. Additionally, a comparison of state-of-the-art automated summary evaluators is conducted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to test how well AI can summarize text has been developed. The UniSumEval benchmark makes sure that the tests are more realistic by using different types of texts and asking for specific details in the summaries. This helps us understand how well different AI models do when given different kinds of information. We also compared some top-performing summary evaluation tools to see which ones work best. |
Keywords
» Artificial intelligence » Summarization
