论文标题
自我监督和控制的多文件意见摘要
Self-Supervised and Controlled Multi-Document Opinion Summarization
论文作者
论文摘要
我们解决了无监督的抽象性摘要，该收集的收集用户生成的评论具有自学和控制。我们提出了一个自制的设置，该设置将单个文档视为一组类似文档的目标摘要。这种设置仅依靠标准的日志样式损失来使训练比以前的方法更简单。我们通过使用控制代码来解决幻觉的问题，以引导一代人走向更连贯和相关的摘要。在本文中，我们扩展了变压器体系结构以允许多个评论作为输入。我们在两个数据集上针对基于图的和最新神经抽象的无监督模型的基准测试表明，我们提出的方法以较高的质量和相关性生成汇总。这在我们的人类评估中得到了确认。这在我们的人类评估中得到了明确的重点，该评估侧重于生成的摘要的忠诚度，我们还提供了一项在控制典型的研究中，并显示了控制典型的典范，并概述了概述的概念属于范围的概念，概念属于幻象的范围，这是我们的概述的重要性。评论。
We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.Finally, we extend the Transformer architecture to allow for multiple reviews as input. Our benchmarks on two datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance.This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries We also provide an ablation study, which shows the importance of the control setup in controlling hallucinations and achieve high sentiment and topic alignment of the summaries with the input reviews.
