Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation

• URL: http://arxiv.org/abs/2303.03608v2
• Date: Thu, 16 Nov 2023 06:13:28 GMT
• Title: Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
• Authors: Yixin Liu, Alexander R. Fabbri, Yilun Zhao, Pengfei Liu, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
• Abstract summary: Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. We develop strong-performing automatic metrics for reference-based summarization evaluation.
• Score: 160.07938471250048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for reference-based summarization evaluation, based on a two-stage evaluation pipeline that first extracts basic information units from one text sequence and then checks the extracted units in another sequence. The metrics we developed include two-stage metrics that can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. We make the developed tools publicly available at https://github.com/Yale-LILY/AutoACU.

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