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Summarization and Personal Information Management

Summarization and Personal Information Management. Carolyn Penstein Ros é Language Technologies Institute/ Human-Computer Interaction Institute. How would you generate a plot summary from movie reviews?. Announcements. Questions? Plan for Today Meng & Wang, 2009 Zhuang et al., 2006.

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Summarization and Personal Information Management

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  1. Summarization and Personal Information Management Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute

  2. How would you generate a plot summary from movie reviews?

  3. Announcements • Questions? • Plan for Today • Meng & Wang, 2009 • Zhuang et al., 2006

  4. I am an eggitarian (veg + egg). I have a particular likeness for Brownies !! :)

  5. Meng & Wang, 2009

  6. Framework: More Applied

  7. Anatomy of an Opinion • Source (whose opinion it is) • Target (what the opinion is about) • Sentiment (what the opinion is) • In this case, specifically an appraisal about the target • This paper is about Target identification • The assumption is that the targets we care about are features of a product (Is this reasonable?)

  8. How would you disect this one? • I finally figured out how to take a rapid fire shot after owning my camera for 3 months. • Source? Target? Opinion?

  9. Architecture Feature Extraction: Extract bigrams of Chinese characters Combine overlapping bigrams where it makes sense Find implicit references to features by association with opinion words (e.g., megapixel refers to a camera) * Which previous paper does this approach remind you of?

  10. What do you think of this? * How many features do you think there should be ideally?

  11. Hu and Liu Approach • Association rule mining of reviews to find sets of words that tend to occur together • Filter out the features that are more likely to be meaningless • Keep the subset that is most compact considering the position the words occur in • Keep the subset of subsuming features • Why not just find common n-grams? • One issue is that this approach tends to see “features” in common words (in reviews) that don’t describe features

  12. Evaluation * Features found in product reviews – doesn’t evaluate the specification trees directly and also doesn’t evaluate the summaries – more of an evaluation of how well they can match parts of reviews to parts of their template.

  13. But will the review be useful?

  14. Zhuang et al.,2006

  15. Example Review

  16. What would you do with this? • “Director Favreau must have been a wiz in shop class, because he loves scenes of welding and nuts and bots. We surely get a lot of attention paid to Tony’s working on his armor.” • What is the opinion being expressed here?

  17. Different Perspectives • Stark discovers that his weapons are not always going to be the good guys, though his associate, reassures him all is okay. So Stark renounces weapons making. • When he finally gets back to the states, Tony is a new man – literally and figuratively. He decides to run his company for peaceful purposes. Obviously that does not get the approval of everyone. • However, after he is captured … and ordered to build his latest weapons masterpiece for them, he realizes how his creations can have a detrimental effect on innocent civilians.

  18. Different Perspectives • Stark discovers that his weapons are not always going to be the good guys, though his associate, reassures him all is okay. So Stark renounces weapons making. • When he finally gets back to the states, Tony is a new man – literally and figuratively. He decides to run his company for peaceful purposes. Obviously that does not get the approval of everyone. • However, after he is captured … and ordered to build his latest weapons masterpiece for them, he realizes how his creations can have a detrimental effect on innocent civilians. Do we see an appraisal here?

  19. What do you think?

  20. Similar Approach to Meng & Wang • Identify feature words and opinion words • Feature words are the common words • Opinion words come from training, wordnet, etc. • Determine class of feature word and polarity of opinion word • Pair feature word with relevant opinion word(s) • Produce a summary, which is a list of feature-opinion pairs

  21. Architecture

  22. Feature Classes

  23. Feature Classes • ELEMENT • [OA] Overall • [ST] Screen Play • [CH] character design • [VP] visual effects • [MS] music and sound • [SE] special effects • PEOPLE • [PR] producer • [PDR] director • [PSC] screen writer • [PAC] actor/acress • [PMS] people in charge of music • [PTC] people in other technical roles

  24. Templates

  25. Dependency Graph * Used during the stage where feature words are paired with opinion words.

  26. Questions • Did they use pronoun resolution? • Only two rules for implicit opinions – adequate? • Where did they get their categories of sentiment from: using proximity to feature nouns might make more sense than frequency • Would a summary that doesn’t have a “bottom line” be useful?

  27. Evaluation

  28. Evaluation * Note the precision/recall trade off

  29. Systematic Errors

  30. Extra Slides

  31. Connections with Appraisal • “Praise is defined as an act which attributes credit to another for some characteristic, attribute, skill, etc., which is positively valued by the writer • More than Attitude, more like Appreciation and Judgment • But more complex – because it confers a judgment of sorts on a product but with reference to the author – so sort of like Engagement • Criticism is also Appreciation and Judgment, but it is negative in tone

  32. Connection with Appraisal • Gives space for neutral discussion of content with reference to the author’s own views • Engagement • You can think of the analysis in this chapter as a less technical application of Appraisal to a specific genre

  33. Questions?

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