@inproceedings{lee-etal-2024-unisumeval,
title = "{U}ni{S}um{E}val: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation for {LLM}s",
author = "Lee, Yuho and
Yun, Taewon and
Cai, Jason and
Su, Hang and
Song, Hwanjun",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.227",
pages = "3941--3960",
abstract = "Existing benchmarks for summarization quality evaluation often lack diverse input scenarios, focus on narrowly defined dimensions (e.g., faithfulness), and struggle with subjective and coarse-grained annotation schemes. To address these shortcomings, we create UniSumEval benchmark, which extends the range of input context (e.g., domain, length) and provides fine-grained, multi-dimensional annotations. We use AI assistance in data creation, identifying potentially hallucinogenic input texts, and also helping human annotators reduce the difficulty of fine-grained annotation tasks. With UniSumEval, we benchmark nine latest language models as summarizers, offering insights into their performance across varying input contexts and evaluation dimensions. Furthermore, we conduct a thorough comparison of SOTA automated summary evaluators. Our benchmark data will be available at https://github.com/DISL-Lab/UniSumEval-v1.0.",
}
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<abstract>Existing benchmarks for summarization quality evaluation often lack diverse input scenarios, focus on narrowly defined dimensions (e.g., faithfulness), and struggle with subjective and coarse-grained annotation schemes. To address these shortcomings, we create UniSumEval benchmark, which extends the range of input context (e.g., domain, length) and provides fine-grained, multi-dimensional annotations. We use AI assistance in data creation, identifying potentially hallucinogenic input texts, and also helping human annotators reduce the difficulty of fine-grained annotation tasks. With UniSumEval, we benchmark nine latest language models as summarizers, offering insights into their performance across varying input contexts and evaluation dimensions. Furthermore, we conduct a thorough comparison of SOTA automated summary evaluators. Our benchmark data will be available at https://github.com/DISL-Lab/UniSumEval-v1.0.</abstract>
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<url>https://aclanthology.org/2024.findings-emnlp.227</url>
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%0 Conference Proceedings
%T UniSumEval: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation for LLMs
%A Lee, Yuho
%A Yun, Taewon
%A Cai, Jason
%A Su, Hang
%A Song, Hwanjun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F lee-etal-2024-unisumeval
%X Existing benchmarks for summarization quality evaluation often lack diverse input scenarios, focus on narrowly defined dimensions (e.g., faithfulness), and struggle with subjective and coarse-grained annotation schemes. To address these shortcomings, we create UniSumEval benchmark, which extends the range of input context (e.g., domain, length) and provides fine-grained, multi-dimensional annotations. We use AI assistance in data creation, identifying potentially hallucinogenic input texts, and also helping human annotators reduce the difficulty of fine-grained annotation tasks. With UniSumEval, we benchmark nine latest language models as summarizers, offering insights into their performance across varying input contexts and evaluation dimensions. Furthermore, we conduct a thorough comparison of SOTA automated summary evaluators. Our benchmark data will be available at https://github.com/DISL-Lab/UniSumEval-v1.0.
%U https://aclanthology.org/2024.findings-emnlp.227
%P 3941-3960
Markdown (Informal)
[UniSumEval: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation for LLMs](https://aclanthology.org/2024.findings-emnlp.227) (Lee et al., Findings 2024)
ACL
- Yuho Lee, Taewon Yun, Jason Cai, Hang Su, and Hwanjun Song. 2024. UniSumEval: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation for LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3941–3960, Miami, Florida, USA. Association for Computational Linguistics.