Comparative Opinion Summarization via Collaborative Decoding
Hayate Iso, Xiaolan Wang, Stefanos Angelidis, Yoshihiko Suhara · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews.While generated summaries offer general and concise information about a particular hotel or product, the information may be insufficient to help the user compare multiple different choices.Thus, the user may still struggle with the question "Which one should I pick?"In this paper, we propose the comparative opinion summarization task, which aims at generating two contrastive summaries and one common summary from two different candidate sets of reviews.We develop a comparative summarization framework COCOSUM, which consists of two base 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 stateof-the-art opinion summarization models.The dataset and code are available at https:// github.com/megagonlabs/cocosum.