How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation

Julius Steen, Katja Markert


Abstract
Manual evaluation is essential to judge progress on automatic text summarization. However, we conduct a survey on recent summarization system papers that reveals little agreement on how to perform such evaluation studies. We conduct two evaluation experiments on two aspects of summaries’ linguistic quality (coherence and repetitiveness) to compare Likert-type and ranking annotations and show that best choice of evaluation method can vary from one aspect to another. In our survey, we also find that study parameters such as the overall number of annotators and distribution of annotators to annotation items are often not fully reported and that subsequent statistical analysis ignores grouping factors arising from one annotator judging multiple summaries. Using our evaluation experiments, we show that the total number of annotators can have a strong impact on study power and that current statistical analysis methods can inflate type I error rates up to eight-fold. In addition, we highlight that for the purpose of system comparison the current practice of eliciting multiple judgements per summary leads to less powerful and reliable annotations given a fixed study budget.
Anthology ID:
2021.eacl-main.160
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1861–1875
Language:
URL:
https://aclanthology.org/2021.eacl-main.160
DOI:
10.18653/v1/2021.eacl-main.160
Bibkey:
Cite (ACL):
Julius Steen and Katja Markert. 2021. How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1861–1875, Online. Association for Computational Linguistics.
Cite (Informal):
How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation (Steen & Markert, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.160.pdf
Dataset:
 2021.eacl-main.160.Dataset.zip
Code
 julmaxi/summary_lq_analysis