GeSERA: General-domain Summary Evaluation by Relevance Analysis

TL;DR

GeSERA is an open-source evaluation method that adapts SERA for the general domain, improving correlation with manual assessments and outperforming ROUGE in some cases.

Contribution

This paper extends SERA from biomedical to general domain by enhancing query reformulation and replacing the index, achieving better correlation with manual evaluations.

Findings

GeSERA outperforms SERA in most cases for general summaries.

GeSERA surpasses ROUGE in two TAC2009 cases.

Index size and human annotations significantly impact evaluation results.

Abstract

We present GeSERA, an open-source improved version of SERA for evaluating automatic extractive and abstractive summaries from the general domain. SERA is based on a search engine that compares candidate and reference summaries (called queries) against an information retrieval document base (called index). SERA was originally designed for the biomedical domain only, where it showed a better correlation with manual methods than the widely used lexical-based ROUGE method. In this paper, we take out SERA from the biomedical domain to the general one by adapting its content-based method to successfully evaluate summaries from the general domain. First, we improve the query reformulation strategy with POS Tags analysis of general-domain corpora. Second, we replace the biomedical index used in SERA with two article collections from AQUAINT-2 and Wikipedia. We conduct experiments with TAC2008, TAC2009, and CNNDM datasets. Results show that, in most cases, GeSERA achieves higher correlations with manual evaluation methods than SERA, while it reduces its gap with ROUGE for general-domain summary evaluation. GeSERA even surpasses ROUGE in two cases of TAC2009. Finally, we conduct extensive experiments and provide a comprehensive study of the impact of human annotators and the index size on summary evaluation with SERA and GeSERA.