Paper Info
Title
EPICURE - Aspect-based Multimodal Review Summarization.
Abstract
Restaurant reviews are popular and a valuable source of information. Often, large number of reviews are written for restaurants which warrants the need for automated summarization systems. In this paper we present epicure, a novel text and image summarization platform. For the summarization of opinionated content like reviews, considering different aspects have largely been ignored, and we address this by creating balanced reviews for different aspects like food and service. We argue that traditional criteria for extractive review summarization such as coverage and diversity have limited applicability. We draw on the power and usefulness of submodular functions for extractive summarization and introduce novel submodular functions such as importance, freshness, purity, trustworthiness and balanced opinion. We are also one of the first to provide an image summary for diffeerent aspects of a restaurant by mapping text to images using a multimodal neural network, for which we provide initial experiments. We show the effectiveness of our platform by evaluating it against strong baselines and also use crowdsourcing experiments for a subjective comparison of our approach with existing works.
Year
DOI
Venue
2018
10.1145/3201064.3202917
WebSci '18: 10th ACM Conference on Web Science Amsterdam Netherlands May, 2018
Keywords
Field
DocType
Multimodal Summarization, Online Reviews, Sentiment Analysis, Sentence-to-Image Mapping, Text Classification, User Study
Astronomy,Image summarization,Automatic summarization,Information retrieval,Computer science,Sentiment analysis,Crowdsourcing,Trustworthiness,Submodular set function,Artificial neural network
Conference
ISBN
Citations 
PageRank 
978-1-4503-5563-6
0
0.34
References 
Authors
15
4
Name
Order
Citations
PageRank
Abhinav Ramesh Kashyap100.68
Christian von der Weth233.42
Zhiyong Cheng354632.55
Mohan Kankanhalli43825299.56