Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

Miguel Arana-Catania, Rob Procter, Yulan He, Maria Liakata


Abstract
We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model.
Anthology ID:
2021.newsum-1.7
Volume:
Proceedings of the Third Workshop on New Frontiers in Summarization
Month:
November
Year:
2021
Address:
Online and in Dominican Republic
Venues:
EMNLP | newsum
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
57–64
Language:
URL:
https://aclanthology.org/2021.newsum-1.7
DOI:
10.18653/v1/2021.newsum-1.7
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.newsum-1.7.pdf