Re-evaluating Evaluation in Text Summarization

• URL: http://arxiv.org/abs/2010.07100v1
• Date: Wed, 14 Oct 2020 13:58:53 GMT
• Title: Re-evaluating Evaluation in Text Summarization
• Authors: Manik Bhandari, Pranav Gour, Atabak Ashfaq, Pengfei Liu and Graham Neubig
• Abstract summary: We re-evaluate the evaluation method for text summarization using top-scoring system outputs. We find that conclusions about evaluation metrics on older datasets do not necessarily hold on modern datasets and systems.
• Score: 77.4601291738445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization. However, while the field has progressed, our standard metrics have not -- for nearly 20 years ROUGE has been the standard evaluation in most summarization papers. In this paper, we make an attempt to re-evaluate the evaluation method for text summarization: assessing the reliability of automatic metrics using top-scoring system outputs, both abstractive and extractive, on recently popular datasets for both system-level and summary-level evaluation settings. We find that conclusions about evaluation metrics on older datasets do not necessarily hold on modern datasets and systems.

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