1. Text Specificity and Impact on Quality of News Summaries Annie Louis & Ani Nenkova University of Pennsylvania June 24, 2011

2.  Texts are a mix of general and specific sentences  Recently, we have developed a classifier that can distinguish general vs. specific sentences  The notion of specificity could be useful for a number of applications In this work, we consider automatic summarization Summaries cannot include all specific content because of the space constraint Understand the role of general/specific content in summaries and how it impacts quality Specificity: amount of detail 2

3.  Seismologists said the volcano had plenty of built-up magma and even more severe eruptions could come later. [overview]  The volcano's activity -- measured by seismometers detecting slight earthquakes in its molten rock plumbing system -- is increasing in a way that suggests a large eruption is imminent, Lipman said. [details] Example general and specific sentences 3

4. Prior studies of general-specific content in summaries  Humans use generalization and specification of source sentences to create abstract sentences  One generation task is to fuse information from key (general) sentence and specific sentence on the same topic to create an abstract sentence  Subtitles of news broadcasts are often generalized compared to original text 4 [Jing & McKeown (2000)] [Wan et al. (2008)] [Marsi et al. (2010)]

5. Overview of our study  Quantitative analysis of specificity in inputs and summaries using a general/specific classifier 1. Human abstracts have much more general content than system extracts 2. Amount of specific content is related to content quality of system summaries More general ~ better 3. Preliminary study on properties of summary- worthy general sentences 5

6. Data: DUC 2002  Generic multidocument summarization task  59 input sets 5 to 15 news documents  3 types of summaries 200 words Manually assigned content and linguistic quality scores 1. Human abstracts 6 2. Human extracts 3. System extracts 2 assessors * 59 9 systems * 59

7. General vs. specific sentence classifier: prior work 7  Sentence level  Features 1. Words 2. Named entities, numbers 3. Likelihood under language model 4. Word specificity 5. Adjectives/adverbs, length of phrases 6. Polar words 7. Sentence length  Training Binary: General or specific Logistic regression: can get probability for a class [Louis & Nenkova (2011)]

8. Classification performance 8  75% accurate Validated on human annotations On examples with high annotator agreement – 90%  The probability is indicative of annotator agreement on class Sentences with high agreement ~ high confidence predictions

9. Computing specificity for a text  Sentences in summary are of varying length, so we compute a score on word level “Average specificity of words in the text” 9 S1:S1:w 12 w 11 …w 13 S2:S2:w 22 w 21 …w 23 S3:S3:w 32 w 31 …w 33 Confidence for being in specific class 0.23 0.81 0.68 0.23 0.81 Average score on tokens Specificity score

10. Average specificity of different types of summaries 1. More general content is preferred in abstracts 2. Simply the process of extraction makes summaries more specific 3. System summaries are overly specific 10 0.7 0.8 0.6 Inputs (0.65) H. Abs (0.62) S.ext (0.74) H.ext (0.72) specific Is the difference related to summary quality? general

11. Analysis of ‘system summaries’: specificity and quality 1. Content quality Importance of content included in the summary More general ~ better 2. Linguistic quality How well-written the summary is perceived to be More specific ~ better 3. Quality of general/specific summaries When a summary is intended to be general or specific 11

12. 1. Specificity and content quality  Coverage score: manually judged at NIST Similarity to a human summary  Correlation with specificity -0.169 (p-value 0.0006)  More specific ~ decreased content quality 12

13. But the correlation is not very high  Specificity is related to realization of content Different from importance of the content  Content quality = content importance + appropriate specificity level  Content importance: ROUGE scores N-gram overlap of system summary and human summary Standard evaluation of automatic summaries 13

14. System summary quality: Specificity as one of the predictors  Coverage score ~ ROUGE-2 (bigrams) + specificity  Linear regression  Weights for predictors in the regression model 14 Mean β Significance (hypothesis β = 0) (Intercept)0.2122.3e-11 ROUGE-21.299< 2.0e-16 Specificity-0.1663.1e-05 Is the combination a better predictor than ROUGE alone?

15. 2. Specificity and linguistic quality  Used different data: TAC 2009 DUC 2002 only reported number of errors Were also specified as a range: 1-5 errors  TAC 2009 linguistic quality score Manually judged: scale 1 – 10 Combines different aspects  coherence, referential clarity, grammaticality, redundancy 15

16. System summaries: What is the avg specificity in different score categories?  More general ~ lower score! General content is useful but need proper context! 16 Ling scoreNo. summaries Poor (1, 2)202 Mediocre (5)400 Best (9, 10)79  If a summary starts as follows: “We are quite a ways from that, actually.” As ice and snow at the poles melt, … Specificity = low Linguistic quality = 1 Average specificity 0.71 0.72 0.77

17. 3. Specificity and quality of general/specific summaries  DUC 2005: General-specific summary task Create general summaries for some inputs, specific summaries for others  How specificity is related to scores of these summaries? 17

18. System summaries: Correlation between specificity and content scores  Further hints that specificity alone is not predictive of summary quality Once a summary is general, level of generality is not longer predictive of quality 18 Summary type Pearson correlation General -0.03 Specific0.18* Content scores were measured using the pyramid method

19. Analysis of general sentences in human summaries 1. Generalization operation performed in human abstracts Frequency of operations, amount of deletions 2. How general sentences are used in human extracts Position, type of sentence 19

20. Data for analysing generalization operation  Aligned pairs of abstract and source sentences conveying the same content Traditional data used for compression experiments  Ziff-Davis corpus 15964 sentence pairs used in Galley & McKeown, 2007 Any number of deletions, up to 7 substitutions  Only 25% abstract sentences are mapped But beneficial to observe the trends 20

21. Generalization operation in human abstracts Transition SS SG GG GS 21 One-third of all transformations are specific to general  Human abstracts involve a lot of generalization No. pairs% pairs 637139.9 567935.6 356222.3 3522.2

22. How specific sentences get converted to general? SG SS GG GS 22 Orig. length 33.5 33.4 21.5 22.7 New/orig length 40.8 56.6 60.8 66.0 Avg. deletions (words) 21.4 16.3 9.3 8.4 Choose long sentences and compress heavily!  A measure of generality would be useful to guide compression Currently only importance and grammaticality are used

23. Use of general sentences in human extracts  Details of Maxwell’s death were sketchy.  Folksy was an understatement.  “Long live democracy!”  Instead it sank like the Bismarck.  Example use of a general sentence in a summary … With Tower’s qualifications for the job, the nominations should have sailed through with flying colors. [Specific] Instead it sank like the Bismarck. [General] … …

24. Simple categorization  75 top general sentences according to classifier confidence 24 Type First sentence Last sentence Attributions Comparisons  General sentences are used as topic/ emphasis sentences Proportion 6 (8%) 13 (17%) 14 (18%) 4 (5%)

25. Conclusion  General sentences are useful content for summaries People use them in summaries for emphasis and topic  They can improve the content quality Choosing good general sentences or generating them will be an interesting task  But linguistic quality should also be considered General sentences difficult to understand out of context Content planning should consider the order of general content

26. Thank you 26

27. Histogram of specificity scores