Abstract
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews. However, generating aspect-specific and general summaries is challenging due to the lack of annotated data. In this work, we propose two simple yet effective unsupervised approaches to generate both aspect-specific and general opinion summaries by training on synthetic datasets constructed with aspect-related review contents. Our first approach, Seed Words Based Leave-One-Out (SW-LOO), identifies aspect-related portions of reviews simply by exact-matching aspect seed words and outperforms existing methods by 3.4 ROUGE-L points on SPACE and 0.5 ROUGE-1 point on OPOSUM+ for aspect-specific opinion summarization. Our second approach, Natural Language Inference Based Leave-One-Out (NLI-LOO) identifies aspect-related sentences utilizing an NLI model in a more general setting without using seed words and outperforms existing approaches by 1.2 ROUGE-L points on SPACE for aspect-specific opinion summarization and remains competitive on other metrics.
Abstract (translated)
观点总结提供了总结大量评论意见的重要解决方案。然而,由于缺乏标注数据,生成特定方面的一般和总体观点总结是一项挑战。在本研究中,我们提出了两个简单但有效的未监督方法,通过训练基于与特定评论内容相关的词汇的合成数据集,生成特定方面的一般和总体观点总结。我们的第一种方法是基于词干选择 leave-one-out (SW-LOO),仅通过匹配词干来识别评论中的相关部分,在空间上比现有方法高出3.4 ROUGE-L点,在OPOSUM+上高出0.5 ROUGE-1点,对于特定观点总结表现优异。我们的第二种方法是基于自然语言推断 leave-one-out (NLI-LOO),在没有词干的情况下使用NLP模型识别相关句子,在空间上比现有方法高出1.2 ROUGE-L点,对于特定观点总结表现优异,在其他指标上仍然竞争力。
URL
https://arxiv.org/abs/2303.11660
