Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023)

Abstract: Opinion summarization is expected to digest larger review sets and provide summaries from different perspectives. However, most existing solutions are deficient in epitomizing extensive reviews and offering opinion summaries from various angles due to the lack of designs for information selection. To this end, we propose SubSumm, a supervised summarization framework for large-scale multi-perspective opinion summarization. SubSumm consists of a review sampling strategy set and a two-stage training scheme. The sampling strategies take sentiment orientation and contrastive information value into consideration, with which the review subsets from different perspectives and quality levels can be selected. Subsequently, the summarizer is encouraged to learn from the sub-optimal and optimal subsets successively in order to capitalize on the massive input. Experimental results on AmaSum and Rotten Tomatoes datasets demonstrate that SubSumm is adept at generating pros, cons, and verdict summaries from hundreds of input reviews. Furthermore, our in-depth analysis verifies that the advanced selection of review subsets and the two-stage training scheme are vital to boosting the summarization performance.

## introduction a plethora of online resources has been appealing for techniques of automatic information mining. opinion summarization, as a task of generalizing views from a group of documents (e.g., reviews, posts, and discussions) related to an entity and presenting them in text form, has received considerable attention. the summarization of user opinions is of great advantage to public opinion research, marketing analysis, and decision-making @xcite . while circumventing the tedious documentby-document browsing, it also offers more significant details compared to a single sentiment rating @xcite . ## methodology we introduce subsumm, a supervised framework for large-scale and multi-perspective opinion summarization, as illustrated in fig. 1 . subsumm is composed of a review sampling strategy set regarding sentiment orientation and information value, as elucidated in sec. 3.1; and a two-stage training scheme, where contrastive learning with candidate summaries is extra performed, see sec. 3.2. given the entity set and the corresponding sample set, for every sample {r 1:n , s}, the goal of opinion summarization is to learn a function f that takes the review set as input and outputs a summary as close as possible to the reference: ## conclusion in this paper, we put forward a supervised summarization framework for large-scale and multiperspective opinion summarization, subsumm. subsumm supports a two-stage training scheme based on a set of review sampling strategies of multiple quality levels. our model surpasses the state-of-the-art models and llm-related systems on amasum and rt, manifesting its superiority in dealing with plentiful reviews and displaying various points of view. the analysis experiments verify that both components of subsumm help the summarizer achieve better results. in the future, we are planning to ( 1 ) explore more review sampling strategies to fully learn the aspect information and (2) combine the proposed framework with llms and generalize it to other large-scale multi-input tasks.