Rationale-based Opinion Summarization (2024)

Abstract: Opinion summarization aims to generate concise summaries that present popular opinions of a large group of reviews. However, these summaries can be too generic and lack supporting details. To address these issues, we propose a new paradigm for summarizing reviews, rationale-based opinion summarization. Rationale-based opinion summaries output the representative opinions as well as one or more corresponding rationales. To extract good rationales, we define four desirable properties: relatedness, specificity, popularity, and diversity and present a Gibbs-sampling-based method to extract rationales. Overall, we propose RATION, an unsupervised extractive system that has two components: an Opinion Extractor (to extract representative opinions) and Rationales Extractor (to extract corresponding rationales). We conduct automatic and human evaluations to show that rationales extracted by RATION have the proposed properties and its summaries are more useful than conventional summaries. The implementation of our work is available at https://github.com/leehaoyuan/RATION.

## introduction online reviews are useful for both customers and businesses @xcite . however, the large number of reviews on such platforms makes it difficult to manually read all of them. opinion summarization aims to tackle this problem by generating a concise summary of the reviews. recently, much progress has been made in opinion summarization, especially unsupervised summarization. these works either extract sentences from reviews as summaries @xcite @xcite @xcite or generate summaries conditioned on reviews @xcite . however, such summaries are usually very generic and lack supporting evidence. to address this issue, @xcite ; @xcite ; @xcite produce summaries in which the summarizing content is attributed to a group of supporting review sentences. however, since their goal is to explain the choice of the summary content, the sizes of these groups of supporting sentences are too large to be useful for user consumption. in this paper, we propose a new paradigm for summarizing reviews, rationale-based opinion summarization. given a set of reviews about an entity (such as a hotel), rationale-based opinion summarization outputs representative opinions summarizing the reviews as well as one or more rationales for each representative opinion. fig. 1 shows an example of a conventional summary produced by a recent extractive summarization model (top) and a rationale-based summary (bottom) containing representative opinions (in blue) and corresponding rationales (in green) for the same entity, a hotel in this case. for illustration, we show only one rationale per representative opinion in the figure but in practice, there can be several such rationales specified by users. such rationale-based summaries can be more useful to users by providing representative opinions as well as informative rationales for them, helping users in making decisions. rationale-based opinion summarization presents several major challenges: (i) what makes a good rationale? and (ii) how to extract rationales? to address the first challenge, we define four desirable properties for rationales: relatedness, specificity, popularity, and diversity. to address the second challenge, we present methods to estimate these properties for review sentences and a gibbssampling-based approach to extract review sentences that can serve as rationales. overall, we propose ration (see fig. 2 ), an unsupervised extractive system that has two components: an opinion extractor (to extract representative opinions) and a rationales extractor (to extract corresponding rationales). both the representative opinions and corresponding rationales are extracted from the input review sentences in an unsupervised manner and are presented together as the final output summary. the opinion extractor extracts representative opinions about various aspects of the entity in a concise manner and removes redundancy in them through a graph-based approach. the rationales extractor first estimates the four above-mentioned properties of good rationales. since there is no supervision in the review domain for estimating some of these properties, ration uses an alignment model fine-tuned to the domain of reviews using artificially constructed samples. the values of these properties collectively represent the joint probability of a set of review sentence to serve as rationales. for each representative opinion, ration uses gibbs sampling to sample a user-specified number of sentences as rationales by approximating this joint probability distribution. our experiments show that rationale-based opinion summaries generated by ration are more informative and useful than conventional summaries and the rationales generated by ration are better than those generated by strong baselines. our contributions are three-fold: ## related work there are generally two types of opinion summarization: abstractive and extractive.for abstractive summarization, previous works either use aggregate review sentence representations @xcite or generate synthetic datasets to train generation models in a supervised setting @xcite ; amplayo and lapata, 2020). for extractive summarization, previous works generally predict the salience of review sentences based on their distance from the aspect representation @xcite , from the average sentence representation @xcite or from the aspect cluster centers @xcite and extract salient sentences as summaries. however, opinion summaries generated by previous works are usually generic and lack supporting evidence. to generate more specific opinion summaries, @xcite generates summaries based on the convex aggregation of review sentence representations instead of the average. however, such summaries might still lack supporting evidence. for explainability, @xcite cluster the opinions extracted from review sentences and generate summaries based on the clusters. @xcite matches review sentences to key points and extracts the key points that are matched by most review sentences as summaries. @xcite generates path representation for each review sentence and generates summaries based on the selected paths. these works can attribute their summary content to a group of review sentences. however, since their goal is to explain the choice of the summary content, the sizes of these groups of supporting sentences are too large to be useful for user consumption. ration aims to address this issue by generating rationale-based opinion summarization where each opinion is supported by a small group of rationales. ## problem statement the input in rationale-based opinion summarization is a set of review sentences @xmath0, ..., s n } of a given entity, such as a hotel. the output is a summary d that consists of representative opinions @xmath1, ..., o m } and corresponding sets of rationales @xmath2, r 2 ...r m }, where r @xmath3,1 , r i,2 ...r i,k }, r i, * is a rationale, and k is specified by the user. see fig. 1 for examples of representative opinions and rationales. the staff was friendly and helpful and called as soon … relatedness specificity popularity diversity gibbs sampling staff is professional: the staff (sylvia) …. location is great: you can't beat location … room is clean: our room was clean … … extractive summarizer rationale extractor opinion extractor input: reviews output:summary ration graph-based redundancy reducer figure 2 ## ration ration addresses this problem using two components: an opinion extractor ( §4.1) and a rationales extractor ( §4.2). the representative opinions and rationales are extracted from the input review sentences in an unsupervised manner. they are combined to form a summary, d ( §4.3). ration uses an alignment model in its processing which is described in §4.4. ## empirical evaluation we now describe experiments to evaluate ration . ## conclusion we propose rationale-based opinion summarization, a new paradigm for summarizing reviews. the rationale-based summaries present representative opinions and their corresponding rationales. we define four desirable properties of rationales: relatedness, specificity, popularity, and diversity. based on these properties, we propose ration , an unsupervised extractive system that extracts representative opinions and their corresponding rationales based on gibbs sampling. our experiments show that rationale-based summaries generated by ration are more useful than conventional opinion summaries. our experiments also show that the rationales generated by ration outperform its variants and strong baselines.