Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.
翻译:以往关于审查总结的工作侧重于衡量对被审查产品或商业主要方面的看法,或建立文字摘要,这些方法仅提供了部分数据观点:基于方面情绪摘要缺乏对方面评级的充分解释或理由,而文本摘要没有量化每个要素的重要性,也不适合反映相互矛盾的观点。最近,提出了关键点分析(KPA),作为提供数据主要点的文字和数量摘要的汇总框架。我们调整KPA,通过引入集体关键点采矿来审查数据,以更好地提取关键点;将情绪分析纳入KPA;为审查摘要确定良好的关键点候选人;利用大量现有审查及其元数据。我们从经验上表明,KPA的这些新扩展大大改善了业绩。我们证明,在没有特定领域说明的情况下,可以实现有希望的成果,而人类监督可以导致进一步的改进。