论文标题
舆论 - 意见：意见摘要的简单框架
OpinionDigest: A Simple Framework for Opinion Summarization
论文作者
论文摘要
我们提出了一种抽象意见摘要框架的意见-Digest，它不依赖于金标准的摘要进行培训。该框架使用基于方面的情感分析模型从评论中提取意见短语，并训练变压器模型从这些提取中重建原始评论。在摘要时，我们合并了多个评论的提取，并选择最受欢迎的评论。选定的观点被用作训练有素的变压器模型的输入，这将它们用作意见摘要。舆论用途还可以根据其方面和/或情感过滤所选意见来生成针对特定用户需求量的定制摘要。对Yelp数据的自动评估表明，我们的框架的表现优于竞争基线。关于两个语料库的人类研究证明，观点灾难源会产生信息的摘要，并显示出有希望的自定义功能。
We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment. Automatic evaluation on Yelp data shows that our framework outperforms competitive baselines. Human studies on two corpora verify that OpinionDigest produces informative summaries and shows promising customization capabilities.
