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Product Review Summarization

Product Review Summarization. Ly Duy Khang. Outline. Motivation Problem statement Related works Baseline Discussion. 1. Motivation (1). A rapid expansion of e-commerce, where more and more products are sold via online portals (Amazon, eBay … )

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Product Review Summarization

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  1. Product Review Summarization Ly DuyKhang

  2. Outline • Motivation • Problem statement • Related works • Baseline • Discussion

  3. 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

  4. 1. Motivation (2)

  5. 2. Problem statement • Given a set of reviews of a product, produce an abstractive summary that captures users’ opinions about that product

  6. 3. Related works (1) • Single-document summarization • Extractive-based approach • Sentence score + ranking • Machine learning technique • Abstractive-based approach • Template • Concept hierarchy

  7. 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

  8. 3. Related works (3) • Sentiment analysis • Reviews polarity classification • PROS/ CONS identification • Mining review opinions • Identify product facets • Identify opinion orientation on the facet

  9. 4. Baseline (1) • Extractive based summary • An integration between Liu et. al. (2004) and NUS - DUC 2005

  10. 4. Baseline (2)

  11. 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

  12. 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

  13. 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

  14. 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

  15. 5. Discussion (1) • Evaluation • We plan to carry on human evaluation.

  16. 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

  17. References [1] 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, 1999. [2] 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 550-557, 1999. [3] I. Mani and M. T. Maybury. Advances in automatic text summarization. 1999. [4] R. Mooney and G. DeJong. Learning schemata for natural language processing. Strategied for Natural Lanaguage Processing, pages 146 - 176. [5] E. Hovy and C. Lin. Automated text summarization in SUMMARIST. Advances in Automatic Text Summarization, 94, 1999.

  18. [6] 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 168-177, 2004. [7] M. Hu and B. Liu. Mining opinion features in customer reviews. Proceedings of the National Conference on Articial Intelligence, page 755760, 2004. [8] S. Ye, L. Qiu, T. S. Chua, and M. Y. Kan. NUS at DUC 2005: Understanding Documents via Concept Links. Document Understanding Conference (DUC05), 2005. [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.

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