Giuseppe Carenini, Raymond T. Ng,

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Multi-Document Summarization of Evaluative Text

• Giuseppe Carenini, Raymond T. Ng,
• Adam Pauls
• Computer Science Dept.
• University of British Columbia
• Vancouver, CANADA

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Multi-Document Summarization of Evaluative Text

• Giuseppe Carenini, Raymond T. Ng,
• Adam Pauls
• Computer Science Dept.
• University of British Columbia
• Vancouver, CANADA

3
Motivation and Focus

• Large amounts of info expressed in text form is
  constantly produced
• News, Reports, Reviews, Blogs, Emails.
• Pressing need to summarize
• Considerable work but limited factual info

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Our Focus

• Evaluative documents (good vs. bad, right vs.
  wrong) about a single entity
• Customer reviews (e.g. Amazon.com)
• Travel logs about a destination
• Teaching evaluations
• User studies (!)
• .
• .
• .

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Our Focus

• We want to do this

The Canon G3 is a great camera. . .
Most users liked the Canon G3. Even though some
did not like the menus, many . . .
Though great, the G3 has bad menus. . .
I love the Canon G3! It . . .
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Two Approaches

• Automatic summarizers generally produce two types
  of summaries
• Extracts A representative subset of text from
  the original corpus
• Abstracts Generated text which contains the most
  relevant info from the original corpus

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Two Approaches (cont'd)

• Extracts-based summarizers generally fare better
  for factual summarization (c.f. DUC 2005)
• But extracts aren't well suited to capturing
  evaluative info
• Can't express distribution of opinions
  (some/all)
• Can't aggregate opinions either numerically or
  conceptually
• So we tried both

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Two Approaches (cont'd)

• Extract-based approach (MEAD)
• Based on MEAD (Radev et al. 2003) framework for
  summarization
• Augmented with knowledge of evaluative info (I'll
  explain later)
• Abstract-based (SEA)
• Based on GEA (Carenini Moore, 2001) framework
  for generating evaluative arguments about an
  entity

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Pipeline Approach (for both)
Shared
Organization
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Extracting evaluative info

• We adopt previous work of Hu Liu (2004) (but
  many others exist . . .)
• Their approach extracts
• What features of the entity are evaluated
• The strength and polarity of the evaluation on
  the -3 .. 3 interval
• Approach is (mostly) unsupervised

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Examples

• the menus are easy to navigate and the buttons
  are easy to use. it is a fantastic camera
• the canon computer software used to download ,
  sort , . . . is very easy to use. the only two
  minor issues i have with the camera are the lens
  cap ( it is not very snug and can come off too
  easily). . . .

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Feature Discovery

• the menus are easy to navigate and the buttons
  are easy to use. it is a fantastic camera
• the canon computer software used to download
  , sort , . . . is very easy to use. the only two
  minor issues i have with the camera are the lens
  cap ( it is not very snug and can come off too
  easily). . . .

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Strength/Polarity Determination

• the menus are easy to navigate(2) and the
  buttons are easy to use(2). it is a
  fantastic(3) camera
• the canon computer software used to download
  , sort , . . . is very easy to use (3). the only
  two minor issues i have with the camera are the
  lens cap ( it is not very snug (-2) and can come
  off too easily (-2))...

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Pipeline Approach (for both)
Shared
Organization
Partially shared
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Organizing Extracted Info

• Extraction provides a bag of features
• But
• features are redundant
• features may range from concrete and specific
  (e.g. resolution) to abstract and general (e.g.
  image)
• Solution map features to a hierarchy Carenini,
  Ng, Zwart 2005

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Feature Ontology
canon
canon g3
digital camera
Canon G3 Digital Camera

• -1,-1,1,2,2,3,3, 3

User Interface
Convenience
. . .
1
Menu
Battery
Buttons
Menus
Lever
1
2,2,2,33
Battery Life
Battery Charging System
-1,-1,-2
. . .
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Organization SEA vs. MEAD

• SEA operates only on the hierarchical data and
  forgets about raw extracted features
• MEAD operates on the raw extracted features and
  only uses hierarchy for sentence ordering (I'll
  come back to this)

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Pipeline Approach (for both)
Shared
Organization
Partially shared
Not shared
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Feature Selection SEA
psk

• -1,-1,1,2,2,3,3, 3

Canon G3 Digital Camera
User Interface
Convenience
1
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Selection Procedure

• Straightforward greedy selection would not work
• if a node derives most of its importance from
  its child(ren) including both the node and the
  child(ren) would be redundant

Similar to redundancy reduction step in many
automatic summarization algorithms
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Feature Selection MEAD

• MEAD selects sentences, not features
• Calculate score for each sentence si with

the menus are easy to navigate(2) and the
buttons are easy to use(2).
feature(si)
psk

• Break ties with MEAD centroid (common feature in
  multi-document summarization)

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Feature Selection MEAD

• We want to extract sentences for most important
  features, and only one sentence per feature
• Put each sentence in bucket for each feature(si)

I like the menus . . .
the menus are easy to navigate(2 ) and the
buttons are easy to use(2 ).
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Feature Selection MEAD

• Take the (single) highest scoring sentence from
  the fullest buckets until desired summary
  length is reached

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Pipeline Approach (for both)
Shared
Organization
Partially shared
Not shared
Not shared
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Presentation MEAD

• Display selected sentences in order from most
  general (top of feature hierarchy) to most
  specific
• That's it!

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Presentation SEA

• SEA (Summarizer of Evaluative Arguments) is based
  on GEA
  (Generator of Evaluative Arguments) (Carenini
  Moore, 2001)
• GEA takes as input
• a hierarchical model of features for an entity
• objective values (good vs. bad) for each feature
  of the entity
• Adaptation is (in theory) straightforward

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Possible GEA Output

• The Canon G3 is a good camera. Although the
  interface is poor, the image quality is
  excellent.

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Target SEA Summary

• Most users thought Canon G3 was a good camera.
  Although, several users did not like interface,
  almost all users liked the image quality.

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Extra work

• What GEA gives us
• High-level text plan (i.e. content selection and
  ordering)
• Cue phrases for argumentation strategy (In
  fact, Although, etc.)
• What GEA does not give us
• Appropriate micro-planning (lexicalization)
• Need to give indication of distribution of
  customer opinions

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Microplanning (incomplete!)

• We generate one clause for each selected feature
• Each clause includes 3 key pieces of information
• Distribution of customers who evaluated the
  feature (Many, most, some etc.)
• Name of the feature (menus, image quality,
  etc.)
• Aggregate of opinions (excellent, fair,
  poor, etc.)

• most users found the menus to be poor

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Microplanning

• Distribution is (roughly) based on fraction of
  customers who evaluated the feature (
  disagreement . . . )
• Name of the feature is straightforward
• Aggregate of opinions is based on a function
  similar in form to the measure of importance
• average polarity/strength over all evaluations
  rather than summing

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Microplanning

• We glue clauses together using cue phrases from
  GEA
• Also perform basic aggregation

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Formative Evaluation

• Goal test users perceived effectiveness
• Participants 28 ugrad students
• Procedure
• Pretend worked for manufacturer
• Given 20 reviews (from either Camera or DVD
  corpus) and asked to generate summary (100
  words) for marketing dept
• After 20 mins, given a summary of the 20 reviews
• Asked to fill out questionnaire assessing summary
  effectiveness (multiple choice and open form)

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Formative Evaluation (cont'd)

• Conditions User given one of 4 summaries
• Topline summary (human)
• Baseline summary (vanilla MEAD)
• MEAD summary
• SEA summary

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Quantitative Results

• Responses on a scale from 1 (Strongly disagree)
  to 5 (Strongly agree)

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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Quantitative Results
Responses on a scale from 1 (Strongly disagree)
to 5 (Strongly agree)
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Qualitative Results MEAD

• Surprising many participants didn't notice or
  didn't mind verbatim text extraction
• Two major complaints about content
• Summary was not representative (negative sentence
  extracted even though majority were positive)
• Evaluations of some features were repeated
• (2) could be addressed, but (1) can only
  partially be fixed with pure extraction

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Qualitative Results SEA

• Some complaints about robotic feel of summary,
  and about repetition/lack of pronouns
• Need to do more complex microplanning
• Some wanted more details (which manual features
  . . . )
• Note this complaint absent with MEAD
• Some disagreed with feature selection
  (precision/recall), but this is a problem even
  with human summaries

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Conclusions

• Extraction works surprisingly well even for
  evaluative summarization
• Topline MEAD _at_ SEA Baseline
• Need to combine strengths of SEA and MEAD for
  evaluative summarization
• Need detail, variety, and natural-sounding text
  provided by extraction
• Need to generate opinion distributions
• Need argument structure from SEA (?)

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Other Future Work

• Automatically induce feature hierarchy
• Produce summaries tailored to user preferences of
  the evaluated entity
• Summarize corpora of evaluative documents about
  more than one entity

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Examples

• MEAD Bottom line , well made camera , easy to
  use, very flexible and powerful features to
  include the ability to use external flash and
  lense / filters choices . It has a beautiful
  design , lots of features, very easy to use ,
  very configurable and customizable , and the
  battery duration is amazing! Great colors ,
  pictures and white balance. The camera is a dream
  to operate in automode , but also gives
  tremendous flexibility in aperture priority ,
  shutter priority, and manual modes . I d highly
  recommend this camera for anyone who is looking
  for excellent quality pictures and a combination
  of ease of use and the flexibility to get
  advanced with many options to adjust if you like.

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Examples

• SEA Almost all users loved the Canon G3 possibly
  because some users thought the physical
  appearance was very good. Furthermore, several
  users found the manual features and the special
  features to be very good. Also, some users liked
  the convenience because some users thought the
  battery was excellent. Finally, some users found
  the editing/viewing interface to be good despite
  the fact that several customers really disliked
  the viewfinder . However, there were some
  negative evaluations. Some customers thought the
  lens was poor even though some customers found
  the optical zoom capability to be excellent.
• Most customers thought the quality of the images
  was very good.

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Examples

• MEAD I am a software engineer and am very keen
  into technical details of everything i buy , i
  spend around 3 months before buying the digital
  camera and i must say , g3 worth every single
  cent i spent on it . I do nt write many reviews
  but i m compelled to do so with this camera . I
  spent a lot of time comparing different cameras ,
  and i realized that there is not such thing as
  the best digital camera . I bought my canon g3
  about a month ago and i have to say i am very
  satisfied .

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Examples

• Human The Canon G3 was received exceedingly
  well. Consumer reviews from novice photographers
  to semi-professional all listed an impressive
  number of attributes, they claim makes this
  camera superior in the market. Customers are
  pleased with the many features the camera offers,
  and state that the camera is easy to use and
  universally accessible. Picture quality, long
  lasting battery life, size and style were all
  highlighted in glowing reviews. One flaw in the
  camera frequently mentioned was the lens which
  partially obstructs the view through the view
  finder, however most claimed it was only a minor
  annoyance since they used the LCD screen.

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Microplanning

• We glue clauses together using cue phrases from
  GEA
• Although, however, etc. indicate opposing
  evidence
• Because, in particular, indicate supporting
  evidence
• Furthermore indicates elaboration
• Also perform basic aggregation

most users found the menus to be poor
most users found the buttons to be poor
most users found the menus and buttons to be poor