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Generating Comparative Summaries of Contradictory Opinions in Text

Generating Comparative Summaries of Contradictory Opinions in Text. (CIKM09’)Hyun Duk Kim, ChengXiang Zhai 2010/05/24 Yu-wen,Hsu. Outline. Introduction Problem Definition An Optimization Framework Similarity Functions Optimization Algorithms Experiment Design & Result Conclusion.

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Generating Comparative Summaries of Contradictory Opinions in Text

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  1. Generating Comparative Summaries of Contradictory Opinions in Text (CIKM09’)Hyun Duk Kim, ChengXiang Zhai 2010/05/24 Yu-wen,Hsu

  2. Outline • Introduction • Problem Definition • An Optimization Framework • Similarity Functions • Optimization Algorithms • Experiment Design & Result • Conclusion

  3. Introduction • opinionated text often contains both positive and negative opinions about a topic makes it even harder to accurately digest mixed opinions. • the battery life [of iPhone] has been excellent I can tell you that I was very disappointed with the 3G [iPhone] battery life • the battery life is good when I rarely use button the battery life is bad when I use button a lot

  4. propose to automatically generate a comparative summary of contradictory opinions • help a user to understand possibly different conditions under which the specific polarity of opinions is expressed • this summarization problem has not been addressed in the existing work, and we call it contrastive opinion summarization (COS)

  5. Problem Definition • OPINIONATED SENTENCE • A sentence is an opinionated sentence if it expresses either a positive or a negative opinion. • CONTRASTIVE SENTENCE PAIR • (x, y) is called a contrastive sentence pair • sentence x and sentence y :the same topic but opposite sentiment

  6. CONTRASTIVE OPINION SUMMARIZATION • : a set of positive sentences • : a set of negative sentences • The task of contrastive opinion summarization (COS) is to generate k contrastive sentence pairs:

  7. An Optimization Framework • based on two basic similarity measures defined on a pair of sentences. • the content similarity of two sentences in the same group of opinions • the contrastiveness of a positive sentence and a negative sentence.

  8. CONTENT SIMILARITY FUNCTION • and :the same polarity, the content similarity function • CONTRASTIVE SIMILARITY FUNCTION • and :opposite polarities, the contrastive similarity function

  9. REPRESENTATIVENESS • The representativeness of a contrastive opinion summary • how well the summary S represents the opinions expressed

  10. CONTRASTIVENESS • The contrastiveness of a contrastive opinion summary S measures how well each matches up with in the summary

  11. A good contrastive opinion summary should intuitively have both high representativeness and high contrastiveness

  12. Similarity Functions • :a term similarity function • Word Overlap (WO): iff • Semantic Word Matching (SEM): if ,otherwise • , : the total counts of words in sentences and • :removing negation and adjectives

  13. Optimization Algorithms • Representativeness-First Approximation • achieve this goal by clustering the sentences in X and Y independently to generate k clusters • take the most representative sentence from each cluster

  14. Next we would like to keep constant and optimize • Set , object function • find the solution • The pair that gives the highest • the optimal summary

  15. Contrastiveness-First Approximation • compute for all and • sort these pairs and gradually add a sentence pair to our summary starting with the pair with the highestcontrastive similarity score. • already chosen pairs • We want to choose to maximize the following objective function

  16. choosing the first pair to maximize the “gain function”

  17. Experiment Design & Result • Data Set • 14 tagged product reviews • All the sentences in these data sets have already been manually tagged with product features as well as sentiment polarities

  18. Measures • Precision: The precision of a summary with k contrastive sentence pairs is the percentage of the k pairs that are agreed by a human annotator (contrastive) • Aspect coverage: The aspect coverage of a summary is the percentage of human-aligned clusters covered in the summary (representativeness)

  19. content sim. Effectiveness of removing sentimental words in computing contrastive similarity contrastive sim.

  20. Conclusion • It aims to summarize contradictory or mixed opinions about a topic and generate a list of contrastive pairs of sentences with different sentiment polarities to help users to digest contradictory opinions. • framed the problem as an optimization problem and proposed two approximation methods to solve the optimization problem. • explored different similarity measures in our optimization framework.

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