Comparative Opinion Summarization via Collaborative Decoding

Anonymous

Abstract: Opinion summarization focuses on generating summaries that reflect popular opinions of multiple reviews for a single entity (e.g., a hotel or a product.) While generated summaries offer general and concise information about a particular entity, the information may be insufficient to help the user compare multiple entities. Thus, the user may still struggle with the question ``Which one should I pick?'' In this paper, we propose a {\em comparative opinion summarization} task, which is to generate two contrastive summaries and one common summary from two given sets of reviews from different entities. We develop a comparative summarization framework CoCoSum, which consists of two few-shot summarization models that jointly generate contrastive and common summaries.Experimental results on a newly created benchmark CoCoTrip show that CoCoSum can produce higher-quality contrastive and common summaries than state-of-the-art opinion summarization models.