Recent work on opinion summarization produces general summaries based on a
set of input reviews and the popularity of opinions expressed in them. In this
paper, we propose an approach that allows the generation of customized
summaries based on aspect queries (e.g., describing the location and room of a
hotel). Using a review corpus, we create a synthetic training dataset of
(review, summary) pairs enriched with aspect controllers which are induced by a
multi-instance learning model that predicts the aspects of a document at
different levels of granularity. We fine-tune a pretrained model using our
synthetic dataset and generate aspect-specific summaries by modifying the
aspect controllers. Experiments on two benchmarks show that our model
outperforms the previous state of the art and generates personalized summaries
by controlling the number of aspects discussed in them.

本论文提出了一种基于方面查询的定制摘要生成方法，使用多示例学习模型诱导的方面控制器创建 (review,summary) 对的合成训练数据集，通过微调预训练模型，生成修改方面控制器的方面特定摘要。在两个基准测试中，我们的模型表现优于先前的技术水平，并通过控制讨论的方面数量生成个性化摘要。