Attributable and Scalable Opinion Summarization (2023)

Abstract: We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions based on the frequency of their encodings. We are able to generate both abstractive summaries by decoding these frequent encodings, and extractive summaries by selecting the sentences assigned to the same frequent encodings. Our method is attributable, because the model identifies sentences used to generate the summary as part of the summarization process. It scales easily to many hundreds of input reviews, because aggregation is performed in the latent space rather than over long sequences of tokens. We also demonstrate that our appraoch enables a degree of control, generating aspect-specific summaries by restricting the model to parts of the encoding space that correspond to desired aspects (e.g., location or food). Automatic and human evaluation on two datasets from different domains demonstrates that our method generates summaries that are more informative than prior work and better grounded in the input reviews.

## introduction online review websites are a useful resource when choosing which hotel to visit or which product to buy, but it is impractical for a user to read hundreds of reviews. there has been significant interest in methods for automatically generating summaries or meta-reviews that aggregate the diverse opinions contained in a set of customer reviews about an entity (e.g., a product, hotel or restaurant) into a single summary. early work on opinion summarization extracted reviewers' sentiment about specific features @xcite or selected salient sentences from reviews based on centrality @xcite , while more recent methods based on neural models have used sentence selection in learned feature spaces @xcite or abstractive summarizers that generate novel output @xcite @xcite @xcite . following @xcite , we define opinion summarization, or review aggregation, as the task of generating a textual summary that reflects frequent or popular opinions expressed in a large number of reviews about an entity. systems are extractive if they select sentences or spans from the input reviews to use as the summary, or abstractive if they generate novel output. review aggregation is challenging for a number of reasons. firstly, it is difficult to acquire or create reference summaries, so models are almost always trained without access to gold standard references @xcite . secondly, popular entities may have hundreds of reviews, which can cause computational difficulties if the approach is not scalable. finally, good summaries should be abstractive and not contain unnecessary detail, but should also not hallucinate false information. ideally, a summarization system should be attributable, offering some evidence to justify its output. previous work has either been exclusively extractive (which is inherently attributable and often scalable but leads to unnecessarily specific summaries) or exclusively abstractive (which often scales poorly and hallucinates, e.g., @xcite . we propose a hybrid method, that produces abstractive summaries accompanied by references to input sentences which act as evidence for each output sentence, allowing us to verify which parts of the input reviews were used to produce the output. depicted in figure 1 , we first learn to encode natural language sentences from reviews as paths through a hierarchical discrete latent space. then, given multiple review sentences about a specific entity, we identify common subpaths that figure 1: hercules is trained to encode sentences from reviews as paths through a hierarchical discrete latent space (top). at inference time, we encode all sentences from the input reviews, and identify frequent paths or subpaths to use for the summary (bottom). the consensus opinion from the three example inputs is that the food is good, so the subpath shown in red is repeated; decoding it should result in an output like "good food". our contributions are as follows: ## hierarchical quantized autoencoders a good review aggregation system should identify frequent or common opinions, while abstracting away the details unique to a specific review. this joint requirement motivates our choice of a hierarchical discrete encoding: the discretization allows us to easily identify repeated opinions by counting them, while the hierarchy allows the model to encode high-level information (aspect, sentiment etc.) separately to specific details and phrasings. ## related work previous work has investigated aggregating user opinions in a latent space, but these approaches have generally been purely extractive for discrete spaces @xcite or purely abstractive for continuous spaces @xcite . other approaches have either been supervised @xcite or have selected 'central' reviews to use as proxy summaries for training @xcite ), but they do not explicitly model the aggregation pro-cess. @xcite propose a method for that highlights both common and contrastive opinions. hierarchical vq was introduced by @xcite as 'multistage vq', with a set of codebooks fitted post-hoc to a set of encoding vectors, and further developed as 'residual vq' by @xcite and @xcite . more recently, @xcite and @xcite concurrently proposed methods for learning the codebook jointly with an encoder-decoder model. a form of hierarchical vq has also been proposed in computer vision @xcite , but in their context the hierarchy refers to a stacked architecture rather than to the latent space. a separate line of work has looked at learning hierarchical latent spaces using hyperbolic geometry @xcite , but the encodings are still continuous and not easily aggregated. the recent surge in performance of language models has led to a desire to evaluate whether the information they output is verifiable. @xcite propose a framework for post-hoc annotation of system output to evaluate attributability; we argue that it is better to have systems that justify their output as part of the generation process. ## conclusion we propose hercules, a method for aggregating user reviews into textual summaries by identifying frequent opinions in a discrete latent space. compared to previous work, our approach generates summaries that are more informative, while also scaling to large numbers of input reviews and providing evidence to justify its output. future work could combine the improvements in attributability and scalability of our model with the fluency of systems that model summaries as a single sequence. allowing the model to access the evidence sets during decoding could lead to improved output quality with less hallucination.