Paper Info
Title
A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3178876.3186069
WWW '18: The Web Conference 2018 Lyon France April, 2018
Keywords
Field
DocType
Probabilistic Generative Models, Topic Models, Information Retrieval, Aspect Summarization
Data science,Automatic summarization,World Wide Web,Computer science,Approximate inference,Refresh rate,Topic model,Generative grammar,Decision-making,Text processing,Generative model
Conference
ISBN
Citations 
PageRank 
978-1-4503-5639-8
3
0.37
References 
Authors
19
6
Name
Order
Citations
PageRank
Vineeth Rakesh1121.97
Weicong Ding2332.82
Aman Ahuja3132.57
Nikhil S. Rao417815.75
Yifan Sun530.37
Chandan K. Reddy680373.50