Presentation on theme: "Product Review Summarization Ly Duy Khang. Outline 1.Motivation 2.Problem statement 3.Related works 4.Baseline 5.Discussion."— Presentation transcript:
Product Review Summarization Ly Duy Khang
Outline 1.Motivation 2.Problem statement 3.Related works 4.Baseline 5.Discussion
1. Motivation (1) A rapid expansion of e-commerce, where more and more products are sold via online portals (Amazon, eBay … ) Online product reviews thus become an important resource: – Customers to share and find opinions about products easily – Producers to get certain degrees of feedback
1. Motivation (2)
2. Problem statement Given a set of reviews of a product, produce an abstractive summary that captures users’ opinions about that product
3. Related works (2) Multi-document summarization – Extractive-based approach Sentence score + ranking + MMR + Ordering – Abstractive-based approach Template Concept hierarchy Sentence fusion with paraphrasing rules
3. Related works (3) Sentiment analysis – Reviews polarity classification – PROS/ CONS identification – Mining review opinions Identify product facets Identify opinion orientation on the facet
4. Baseline (1) Extractive based summary An integration between Liu et. al. (2004) and NUS - DUC 2005
4. Baseline (2)
4. Baseline (3) Product facets identification – Association rule mining Each transaction consists of nouns/noun phrases from single sentence The frequent itemsets are the candidate product facets – Redundancy pruning Removing redundant facets that contain only single words. (e.g. life -> battery life) – Compactness pruning Removing meaningless facets that contain multiple words
4. Baseline (4) Sentiment classification – WordNet to grow seed lists of (+) and (-) ADJ – ADJ share the same orientation as their synonyms and opposite orientation as their antonyms
4. Baseline (5) Reviews labeling with facets and polarity – The unit of labeling is sentence – The summation of all these polarities yields the polarity of the whole sentence
4. Baseline (6) Summary generation – Sentences are clustered based on their labeling – For each facet, we produce a summary Sentences are scored based on concept link similarity MMR ranks the sentences
5. Discussion (1) Evaluation – We plan to carry on human evaluation.
5. Discussion (2) In the baseline, – Inherit all problems of extractive-based summary – The unit of sentence is too coarse-grained – Relationship between facets are not addressed
References  V. Hatzivassiloglou, J. L. Klavans, M. L. Holcombe, R. Barzilay, M. Y. Kan, and K. R. Mckeown. SimFinder: A Flexible Clustering Tool for Summarization. Machine Learning,  R. Barzilay, K. R. Mckeown, and M. Elhadad. Information fusion in the context of multidocument summarization. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, page ,  I. Mani and M. T. Maybury. Advances in automatic text summarization  R. Mooney and G. DeJong. Learning schemata for natural language processing. Strategied for Natural Lanaguage Processing, pages  E. Hovy and C. Lin. Automated text summarization in SUMMARIST. Advances in Automatic Text Summarization, 94, 1999.
 M. Hu and B. Liu. Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, page ,  M. Hu and B. Liu. Mining opinion features in customer reviews. Proceedings of the National Conference on Articial Intelligence, page ,  S. Ye, L. Qiu, T. S. Chua, and M. Y. Kan. NUS at DUC 2005: Understanding Documents via Concept Links. Document Understanding Conference (DUC05), [9[ X. Ding, B. Liu, and P. S. Yu. A holistic lexicon-based approach to opinion mining Proceedings of the international conference on Web search and web data mining – WSDM '08, page 231, 2008.