A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews
A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews
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DOI：
10.1145/3178876.3186069
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Vineeth Rakesh;Weicong Ding;Aman Ahuja;Nikhil S. Rao;Yifan Sun;Chandan K. Reddy
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文献类型：
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作者：
Vineeth Rakesh;Weicong Ding;Aman Ahuja;Nikhil S. Rao;Yifan Sun;Chandan K. Reddy
在线评论已成为消费者决策过程中不可避免的一部分，购买的可能性不仅取决于产品的总体评分，还取决于对其各个方面的描述。因此，像亚马逊和沃尔玛这样的电子商务网站不断鼓励用户撰写高质量的评论，并对产品的不同方面进行分类总结。然而，尽管有这些尝试，要浏览数千条评论并寻找能解答消费者疑问的答案仍需付出巨大努力。例如，一个游戏玩家可能有兴趣购买具有高刷新率且支持Gsync和Freesync技术的显示器，而一个摄影师可能对色彩深度和准确性等方面感兴趣。为了应对这些挑战，在本文中，我们提出了一种名为AP SUM的生成式方面总结模型，该模型能够提供在线评论的细粒度总结。为了克服方面稀疏性这一固有问题，我们施加了双重约束：(a) 在文档 - 主题分布上施加尖峰 - 平板先验，以及(b) 在单词 - 主题分布上施加语言监督。通过一系列严格的实验，我们表明所提出的模型能够在各种数据集上优于最先进的方面总结模型，并提供直观的细粒度总结，从而简化消费者的购买决策。
Online reviews have become an inevitable part of a consumer's decision making process, where the likelihood of purchase not only depends on the product's overall rating, but also on the description of its aspects. Therefore, e-commerce websites such as Amazon and Walmart constantly encourage users to write good quality re- views and categorically summarize different facets of the products. However, despite such attempts, it takes a significant effort to skim through thousands of reviews and look for answers that address the query of consumers. For example, a gamer might be interested in buying a monitor with fast refresh rates and support for Gsync and Freesync technologies, while a photographer might be interested in aspects such as color depth and accuracy. To address these chal- lenges, in this paper, we propose a generative aspect summarization model called APSUM that is capable of providing fine-grained sum- maries of online reviews. To overcome the inherent problem of aspect sparsity, we impose dual constraints: (a) a spike-and-slab prior over the document-topic distribution and (b) a linguistic su- pervision over the word-topic distribution. Using a rigorous set of experiments, we show that the proposed model is capable of out- performing the state-of-the-art aspect summarization model over a variety of datasets and deliver intuitive fine-grained summaries that could simplify the purchase decisions of consumers.