Summarizing Contrastive Viewpoints in Opinionated Text

Michael J. Paul, ChengXiang Zhai, Roxana GirjuEMNLP’10

Speaker: Hsin-Lan, WangDate: 2010/12/07

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Outline Introduction Modeling Viewpoints

Topic-Aspect Model Features

Multi-Viewpoint Summarization Comparative LexRank Summary Generation

Experiment and Evaluation Conclusion

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Introduction

The amount of opinionated text available online has been growing rapidly.

In this paper, we study how to summarize opinionated text in a such a way that highlights contrast between multiple viewpionts.

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Introduction

Generate two types of multi-view summaries: macro multi-view summary

Contains multiple sets of sentences, each representing a different viewpoint.

micro multi-view summary Contains a set of pairs of contrastive

sentences.

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Modeling Viewpoints

Challenge: to model and extract viewpoints which are hidden in text.

Solve: Topic-Aspect Model (TAM)

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Modeling Viewpoints

TAM

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Modeling Viewpoints Features

Words baseline approach do not do any stop word removal stemming

Dependency Relations use Stanford parser full-tuple: rel(a,b) split-tuple: rel(a,*), rel(*,b)

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Modeling Viewpoints Features

Negation Rel(wi, wj), if either wi or wj is negated, then w

e simply rewrite it as . Polarity

use Subjectivity Clues lexicon amod(idea, good)→

amod(idea,+) and amod(*,good) →rel(a, － ).

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Modeling Viewpoints

Features Generalized Relations

use Stanford dependencies Rewrite rel(a,b) as Rrel(a,b).

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Multi-Viewpoint Summarization

Comparative LexRank Make it favor jumping to a good represen

tative excerpt x of any viewpoint v. Make it favor jumping between two excer

pts that can potentially form a good contrastive pair.

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Multi-Viewpoint Summarization

Comparative LexRank

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Multi-Viewpoint Summarization Summary Generation

Macro contrastive summarization Using the random walk stationary distribution across

all of the data to rank the excerpts. Separate the top ranked excerpts into two disjoint set

s. Remove redundancy and produce the summary.

Micro contrastive summarization Consist of a pair (xi,xj) with the pairwise relevance scor

e. Rank these pairs and remove redundancy.

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Experiments and Evaluation

Experimental Setup First dataset: 948 verbatim responses to

a Gallup phone survey about the 2010 U.S. healthcare bill.

Second dataset: use the Bitterlemons corpus, a collection of 594 editorials about the Israel-Palestine conflict.

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Experiments and Evaluation

Stage One: Modeling Viewpoints

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Experiments and Evaluation

Stage Two: Summarizing Viewpoints Gold Standard Summaries

Gallup healthcare poll

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Experiments and Evaluation

Stage Two: Summarizing Viewpoints Baseline Approaches

Graph-based algorithms When λ=1, the random walk model only transition

s to sentences within the same viewpoint. The modified algorithm produces the same rankin

g as the unmodified LexRank. Model-based algorithms

Compare against the approach of Lerman and McDonald.

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Experiments and Evaluation

Stage Two: Summarizing Viewpoints Metrics

using the standard ROUGE evaluation metric For evaluating the macro-level summaries:

For evaluating the micro-level summaries:

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Experiments and Evaluation

Stage Two: Summarizing Viewpoints Evaluation Results

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Experiments and Evaluation

Unsupervised Summarization Bitterlemons corpus (without a gold set) Asked 8 people to guess if each viewpoint

’s summary was written by Israeli or Palestinian authors.

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Experiments and Evaluation

Unsupervised Summarization Macro-level summaries:

Correctly labeled 78% of the summary sets.

Micro-level summaries: Many of the sentences are mislabeled,

and the ones that are correctly labeled are not representative of the collection.

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Conclusion

Present steps toward a two-stage system that can automatically extract and summarize viewpoints in opinionated text.

First: the accuracy of clustering documents by viewpoint can be enhanced by using rich dependency features.

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Conclusion

Second: use Comparative LexRank to generate contrastive summaries both at the macro and micro level.