论文标题
嘈杂的配对和部分监督对程式化意见摘要
Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
论文作者
论文摘要
意见摘要研究主要集中于生成摘要，反映了客户评论中的重要意见，而无需对写作风格大量关注。在本文中，我们提出了风格化的意见摘要任务，该任务旨在以所需的（例如专业）写作方式来生成客户评论的摘要。为了解决收集客户和专业评论对的困难，我们开发了一个非并行培训框架，嘈杂的配对和部分监督（NAPA），该框架训练了非平行客户和专业审查集的风格化意见摘要系统。我们通过从Yelp和Michelin收集客户和专业评论来创建基准制定。关于生产和少数杂志的实验结果表明，我们的非平行培训框架始终改善自动和人类评估，成功地构建了一个风格化的意见摘要模型，该模型可以从客户评论中产生专业编写的摘要。该代码可从https://github.com/megagonlabs/napa获得
Opinion summarization research has primarily focused on generating summaries reflecting important opinions from customer reviews without paying much attention to the writing style. In this paper, we propose the stylized opinion summarization task, which aims to generate a summary of customer reviews in the desired (e.g., professional) writing style. To tackle the difficulty in collecting customer and professional review pairs, we develop a non-parallel training framework, Noisy Pairing and Partial Supervision (NAPA), which trains a stylized opinion summarization system from non-parallel customer and professional review sets. We create a benchmark ProSum by collecting customer and professional reviews from Yelp and Michelin. Experimental results on ProSum and FewSum demonstrate that our non-parallel training framework consistently improves both automatic and human evaluations, successfully building a stylized opinion summarization model that can generate professionally-written summaries from customer reviews. The code is available at https://github.com/megagonlabs/napa
