Comparative Opinion Summarization via Collaborative Decoding (2022)

Abstract: 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 state-of-the-art opinion summarization models. The dataset and code are available at https://github.com/megagonlabs/cocosum

## introduction widely available online customer reviews help users with decision-making in a variety of domains (e.g., hotel, restaurant, or company). after creating a list of candidate choices based on initial conditions (e.g., area, price range, restaurant type), the user often has to compare a few choices in depth by carefully reading the reviews to make a final decision. however, it is time-consuming and difficult for the user to detect differences and similarities between the candidates, as those pieces of information are often scattered in different reviews. the recent success of neural summarization techniques and the growth of online review platforms led to establishing the field of multi-document opinion summarization @xcite ; amplayo and lapata, 2020; suhara let's go to the ! ∩ \ reviews of reviews of reviews of \ reviews of ## cocosum in order to summarize contrastive and common opinions from two sets of reviews, the comparative opinion summarization task requires the model to compare and contrast two sets of reviews; however, existing single-entity opinion summarization models do not have such functionality. therefore, we design a "collaborative" decoding solution codecoding, which characterizes the target summary distribution by leveraging two base summarization models. figure 3 : encoder of the base common summarization model has type embeddings to distinguish the original entity. ## conclusions in this paper, we propose a new comparative opinion summarization task, which aims to generate contrastive and common summaries from reviews of a pair of entities, to help the user answer the question "which one should i pick?" to this end, we develop a comparative summarization framework cocosum, which consists of two base summarization models; cocosum also implements co-decoding, which jointly uses the token probability distribution of each model to generate more distinctive summaries in the decoding step. for evaluation, we created a comparative opinion summarization benchmark cocotrip based on the tripadvisor review corpus. experimental results on cocotrip show that cocosum with codecoding significantly outperforms existing opinion summarization models with respect to both summarization quality and distinctiveness. we also confirm that co-decoding successfully augments cocosum, so it can generate more distinctive contrastive and common summaries than other models through comprehensive analysis. emnlp 2021, pages 3885-3903, punta cana, dominican republic. association for computational linguistics. masaru isonuma, junichiro mori, danushka bollegala, and ichiro sakata. 2021. unsupervised abstractive opinion summarization by generating sentences with tree-structured topic guidance. transactions of the association for computational linguistics, 9(0):945-961. robert a. jacobs, michael i. jordan, steven j. nowlan, and geoffrey e. hinton. 1991. adaptive mixtures of local experts. neural computation, 3:79-87. wenjun ke, jinhua gao, huawei 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