跨语言评论摘要框架MARS，自动提炼多语言用户反馈关键点。
MARS: Multilingual Aspect-centric Review Summarisation
- 两步法：先提取观点再生成摘要
- 相比现有方法显著提升摘要质量
- 适合需要多语言洞察的商业分析场景
在多个地区和语言中，客户反馈量持续增长，如何聚合与理解跨语言的用户情感已成为企业的重要挑战。本文提出一种名为MARS的新框架，采用‘提取-再摘要’的两阶段范式，实现领域无关的细粒度多语言评论摘要。通过大量自动评估与人工评测表明，该方法在摘要质量上显著优于现有抽象式基线，并具备实时系统部署的高效性。
原文摘要 · Abstract (English)
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.
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