From COMET to COMES - Can Summary Evaluation Benefit from Translation Evaluation?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10457013" target="_blank" >RIV/00216208:11320/22:10457013 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2022.eval4nlp-1.3.pdf" target="_blank" >https://aclanthology.org/2022.eval4nlp-1.3.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

From COMET to COMES - Can Summary Evaluation Benefit from Translation Evaluation?

Original language description

Comet is a recently proposed trainable neural-based evaluation metric developed to assess the quality of Machine Translation systems. In this paper, we explore the usage of Comet for evaluating Text Summarization systems -- despite being trained on multilingual MT outputs, it performs remarkably well in monolingual settings, when predicting summarization output quality. We introduce a variant of the model -- Comes -- trained on the annotated summarization outputs that uses MT data for pre-training. We examine its performance on several datasets with human judgments collected for different notions of summary quality, covering several domains and languages.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů