UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMs

• URL: http://arxiv.org/abs/2409.19898v2
• Date: Tue, 1 Oct 2024 07:11:44 GMT
• Title: UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMs
• Authors: Yuho Lee, Taewon Yun, Jason Cai, Hang Su, Hwanjun Song,
• Abstract summary: Existing benchmarks for summarization quality evaluation often lack diverse input scenarios and focus on narrowly defined dimensions. We create UniSumEval benchmark, which extends the range of input context and provides fine-grained, multi-dimensional annotations.
• Score: 19.097842830790405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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