Unsupervised Class-Specific Abstractive Summarization of Customer Reviews (2021)

Abstract: Large-scale unsupervised abstractive summarization is sorely needed to automatically scan millions of customer reviews in today’s fast-paced e-commerce landscape. We address a key challenge in unsupervised abstractive summarization – reducing generic and uninformative content and producing useful information that relates to specific product aspects. To do so, we propose to model reviews in the context of some topical classes of interest. In particular, for any arbitrary set of topical classes of interest, the proposed model can learn to generate a set of class-specific summaries from multiple reviews of each product without ground-truth summaries, and the only required signal is class probabilities or class label for each review. The model combines a generative variational autoencoder, with an integrated class-correlation gating mechanism and a hierarchical structure capturing dependence among products, reviews and classes. Human evaluation shows that generated summaries are highly relevant, fluent, and representative. Evaluation using a reference dataset shows that our model outperforms state-of-the-art abstractive and extractive baselines.

## introduction as volume and scope of online customer reviews continue to explode, so does the need for online sellers to digest and draw insights to improve products. today, both sellers and customers manually sift through hundreds of reviews across competing products to decipher systemic or trending concerns from isolated or irrelevant issues. opinion summarization technology run across millions of reviews has drawn much attention due to its potential for streamline defect discovery, trend analysis, product development, provided that summaries are informative and fluent. this work concerns abstractive summarization of product reviews. abstractive summaries which contain new phrases and words not found in original documents are often more fluent, more concise and more informative given the same length than extractive ones which only contain words, phrases and sentences from the original documents. current state-of-the-art methods for abstractive summarization are based on supervised deeplearning language models @xcite @xcite , and rely on large amount of human-written ground-truth summaries. because text summarization systems are domain-sensitive @xcite and ground-truth opinion summaries are expensive to obtain, unsupervised opinion summarization has recently garnered significant attention with noteworthy efforts @xcite @xcite . unfortunately, summaries generated by unsupervised models are often generic and uninformative, and do not provide useful information about different aspects of the product. to make review summaries more useful to users, we propose a class-specific unsupervised abstractive summarization model, which can generate class-specific summaries from multiple reviews of each product, according to any predefined set of topical classes of interest for the users. the model can be trained without using any ground-truth summaries. the only additional signal required for the model is the class probabilities or class label for each review generated by an independent black-box classifier, or provided by annotators. an example set of such topical classes is the set of major issue classes that products might be subjected to, including misleading product description, poor quality, sizing/fit/style issues, etc. see to generate class-specific summaries for arbi-trary classes, existing opinion summarization models require either i) a large training set of groundtruth summaries per class, or ii) a complicated and costly training set comprising multiple types of manual annotations such as token tags, aspectopinion phrase pairs, and phrase labels @xcite . in contrast, our model only needs review-class label pairs. the proposed model class-copycat combines a generative variational autoencoder (vae) model with a hierarchical structure that captures dependence among products, reviews and classes, allowing the representation of class-specific information through class latent variables. we also propose an integrated two-layer filter mechanism consisting of a class-correlation gate and a set of classspecific importance coefficients which focus on class-related words, and thus reduce irrelevant or generic information and increase informativeness with respect to (w.r.t.) each class of interest. our contributions can be summarized as follows: ## proposed abstractive class-specific multi-review summarization we start with a high level description of the proposed model (sec. 3.1), before presenting in greater detail the encoder and decoder subnets (sec. 3.2, 3.3). later, we introduce the loyalty term that discourages summaries containing false information (sec. 3.4), and describe how the trained model generates class-specific summaries (sec. 3.5). ## conclusion we have proposed a model for generating classspecific summaries from a collection of reviews. our evaluation results show that our model outperforms many abstractive and extractive baselines, including state-of-the-art models, in term of rouge scores that measure the semantic overlap between generated and gold summaries. we also show through human evaluation that generated summaries of our model are highly relevant to the classes of interest, fluent, and representative of common opinion.