Summarizing customer feedback to provide actionable insights for products/services at scale is an important problem for businesses across industries. Lately, the review volumes are increasing across regions and languages, therefore the challenge of aggregating and understanding customer sentiment across multiple languages becomes increasingly vital. In this paper, we propose a novel framework involving a two-step paradigm \textit{Extract-then-Summarise}, namely MARS to revolutionise traditions and address the domain agnostic aspect-level multilingual review summarisation. Extensive automatic and human evaluation shows that our approach brings substantial improvements over abstractive baselines and efficiency to real-time systems.
@article{arxiv.2410.09991,
title = {MARS: Multilingual Aspect-centric Review Summarisation},
author = {Sandeep Sricharan Mukku and Abinesh Kanagarajan and Chetan Aggarwal and Promod Yenigalla},
journal= {arXiv preprint arXiv:2410.09991},
year = {2024}
}
Comments
arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission