Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation

TL;DR

This paper introduces interpretable and efficient automatic metrics for reference-based summarization evaluation using a two-stage pipeline that extracts and checks information units, enhancing interpretability and balancing efficiency.

Contribution

The paper presents a novel two-stage evaluation pipeline for summarization metrics that improves interpretability and efficiency, with publicly available tools.

Findings

Two-stage metrics offer high interpretability at both unit and summary levels.

One-stage metrics balance efficiency and interpretability effectively.

Tools are publicly available for adoption and further research.

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.