Product Review Summarization

Ly Duy Khang

Outline

1. Motivation2. Problem statement3. Related works4. Baseline5. 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 (1)

• Single-document summarization– Extractive-based approach• Sentence score + ranking• Machine learning technique

– Abstractive-based approach• Template• Concept hierarchy

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

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and K. R. Mckeown. SimFinder: A Flexible Clustering Tool for Summarization. Machine Learning, 1999.

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[7] M. Hu and B. Liu. Mining opinion features in customer reviews. Proceedings of the National Conference on Articial Intelligence, page 755760, 2004.

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