Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Mining multi-faceted overviews of arbitrary topics in a text collection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Query-Based Summarization of Customer Reviews
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Sentiment summarization: evaluating and learning user preferences
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Multi-aspect opinion polling from textual reviews
Proceedings of the 18th ACM conference on Information and knowledge management
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
A unified graph model for Chinese product review summarization using richer information
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
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In this paper, we study the aspect-based extractive summarization based on the observations that a good summary should present representative opinions on user concerned sub-aspects within limited words. According to these observations, we argue that, two requirements, i.e. representativeness and diversity, should be considered for generating a good summary in addition to the traditional requirements of aspect-relevance and sentiment intensity. We focus on the intrinsic relationship between sentences and the dependency between extracted sentences for summarization, and thus propose a novel aspect-based summarization method for online reviews, which employs an Aspect-sensitive Markov Random Walk Model to meet the representativeness requirement, as well as a greedy redundancy removal method to meet the diversity requirement. The conducted experiments verify the effectiveness of the proposed method by comparing it with the baselines which ignores representativeness and/or diversity. The experimental results also show that, the two requirements we present are both indispensable for a good summary.