Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes
This work addresses the problem of summarizing complex, low-quality multilingual texts for users in deliberative processes, but it is incremental as it applies existing methods to a new dataset.
The study tackled the challenge of summarizing deliberative process texts in non-English languages, which often contain multiple narratives and poor grammar, by using an off-the-shelf machine translation model combined with abstractive summarization models, resulting in promising improvements in fluency, consistency, and relevance of the summaries.
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.