1 multi-document summarization and evaluation. 2 task characteristics input: a set of documents on...

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Multi-document Multi-document Summarization and EvaluationSummarization and Evaluation

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Task CharacteristicsTask Characteristics

Input: a set of documents on the same topic Retrieved during an IR search Clustered by a news browsers Problem: same topic or same event?

Output: a paragraph length summary Salient information across documents Similarities between topics?

Redundancy removal is critical

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Some Standard ApproachesSome Standard Approaches

Salient information = similarities Pairwise similarity between all sentences Cluster sentences using similarity score (Themes) Generate one sentence for each theme

Sentence extraction (one sentence/cluster) Sentence fusion: intersect sentences within a theme and choose

the repeated phrases. Generate sentence from phrases

Salient information = important words Important words are simply the most frequent in the document

set SumBasic simply chooses sentences with the most frequent

words. Conroy expands on this

Daume and Marcu have been the renegades

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Some Variations on TaskSome Variations on Task

Focused-based summarization: given a topic/query generate a summary

Update summaries: given an event over time, tell us what’s new

Multilingual summarization: generate an English summary of multiple documents in different languages

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DUC – Document Understanding DUC – Document Understanding ConferenceConference

Established and funded by DARPA TIDES Run by independent evaluator NIST

Open to summarization community Annual evaluations on common datasets 2001-present

Tasks Single document summarization Headline summarization Multi-document summarization Multi-lingual summarization Focused summarization

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DUC EvaluationDUC Evaluation

Gold Standard Human summaries written by NIST From 2 to 9 summaries per input set

Multiple metrics Manual

Coverage (early years) Pyramids (later years) Responsiveness (later years) Quality questions

Automatic Rouge (-1, -2, -skipbigrams, LCS, BE)

Granularity Manual: sub-sentential elements Automatic: sentences

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Considerations Across EvaluationsConsiderations Across Evaluations

Independent evaluator Not always as knowledgeable as researchers Impartial determination of approach Extensive collection of resources

Determination of task Appealing to a broad cross-section of community Changes over time

DUC 2001-2002 Single and multi-document DUC 2003: headlines, multi-document DUC 2004: headlines, multilingual and multi-document, focused DUC 2005: focused summarization DUC 2006: focused and a new task, up for discussion

How long do participants have to prepare? When is a task dropped?

Scoring of text at the sub-sentential level

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Potential ProblemsPotential Problems

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Comparing Text Against TextComparing Text Against Text

Which human summary makes a good gold standard? Many summaries are good

At what granularity is the comparison made?

When can we say that two pieces of text match?

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Variation impacts evaluationVariation impacts evaluation

Comparing content is hard All kinds of judgment calls

Paraphrases VP vs. NP

Ministers have been exchanged Reciprocal ministerial visits

Length and constituent type Robotics assists doctors in the medical operating theater Surgeons started using robotic assistants

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NightmareNightmare: only one gold standard: only one gold standard

System may have chosen an equally good sentence but not in the one gold standard Pinochet arrested in London on Oct 16 at a Spanish judge’s

request for atrocities against Spaniards in Chile. Former Chilean dictator Augusto Pinochet has been

arrested in London at the request of the Spanish government

In DUC 2001 (one gold standard), human model had significant impact on scores (McKeown et al)

Five human summaries needed to avoid changes in rank (Nenkova and Passonneau)

DUC2003 data 3 topic sets, 1 highest scoring and 2 lowest scoring 10 model summaries

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ScoringScoring

Two main approaches used in DUC

ROUGE (Lin and Hovy)

Pyramids (Nenkova and Passonneau)

Problems: Are the results stable? How difficult is it to do the scoring?

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ROUGE: ROUGE: RRecall-ecall-OOriented riented UUnderstudy for nderstudy for GGisting isting EEvaluationvaluation

Rouge – Ngram co-occurrence metrics measuring content overlap

Counts of n-gram overlaps between candidate and model

summaries

Total n-grams in summary model

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ROUGEROUGE Experimentation with different units of comparison:

unigrams, bigrams, longest common substring, skip-bigams, basic elements

Automatic and thus easy to apply

Important to consider confidence intervals when determining differences between systems Scores falling within same interval not significantly different Rouge scores place systems into large groups: can be hard to

definitively say one is better than another

Sometimes results unintuitive: Multilingual scores as high as English scores Use in speech summarization shows no discrimination

Good for training regardless of intervals: can see trends

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Comparison of Scoring Methods Comparison of Scoring Methods in DUC05in DUC05

Comparisons between Pyramid (original,modified), responsiveness, and Rouge-SU4

Pyramids score computed from multiple humans

Responsiveness is just one human’s judgment Rouge-SU4 equivalent to Rouge-2

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Creation of pyramids Creation of pyramids

Done at Columbia for each of 20 out of 50 sets

Primary annotator, secondary checker

Held round-table discussions of problematic constructions that occurred in this data set

Comma separated lists Extractive reserves have been formed for managed harvesting of

timber, rubber, Brazil nuts, and medical plants without deforestation.

General vs. specific Eastern Europe vs. Hungary, Poland, Lithuania, and Turkey

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Characteristics of the ResponsesCharacteristics of the Responses

Proportion of SCUs of Weight 1 is large 44% (D324) to 81% (D695)

Mean SCU weight: 1.9

Agreement among human responders is quite low

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# of SCUs at each weight

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Human performance/Best sysHuman performance/Best sys

Pyramid Modified Resp ROUGE-SU4

B: 0.5472 B: 0.4814 A: 4.895 A: 0.1722 A: 0.4969 A: 0.4617 B: 4.526 B: 0.1552~~~~~~~~~~~~~~~~~

14: 0.2587 10: 0.2052 4: 2.85 15: 0.139 Best system ~50% of human performance on manual metrics

Best system ~80% of human performance on ROUGE

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Pyramid original Modified Resp Rouge-SU414: 0.2587 10: 0.2052 4: 2.85 15: 0.139 17: 0.2492 17: 0.1972 14: 2.8 4: 0.134 15: 0.2423 14: 0.1908 10: 2.65 17: 0.1346 10: 0.2379 7: 0.1852 15: 2.6 19: 0.1275 4: 0.2321 15: 0.1808 17: 2.55 11: 0.1259 7: 0.2297 4: 0.177 11: 2.5 10: 0.127816: 0.2265 16: 0.1722 28: 2.45 6: 0.1239 6: 0.2197 11: 0.1703 21: 2.45 7: 0.1213 32: 0.2145 6: 0.1671 6: 2.4 14: 0.1264 21: 0.2127 12: 0.1664 24: 2.4 25: 0.1188 12: 0.2126 19: 0.1636 19: 2.4 21: 0.1183 11: 0.2116 21: 0.1613 6: 2.4 16: 0.1218 26: 0.2106 32: 0.1601 27: 2.35 24: 0.118 19: 0.2072 26: 0.1464 12: 2.35 12: 0.116 28: 0.2048 3: 0.145 7: 2.3 3: 0.1198 13: 0.1983 28: 0.1427 25: 2.2 28: 0.1203 3: 0.1949 13: 0.1424 32: 2.15 27: 0.110 1: 0.1747 25: 0.1406 3: 2.1 13: 0.1097

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QuestionsQuestions

Brotzman: In "Topic-Focused Multi-document Summarization Using an Approximate Oracle Score" and "Bayesian Query-Focused Summarization" we read of two methods of document summarization that rely on a surface-level representation of written language. They both beg the question (and Nenkova hints at the issue by characterizing the DUC's "coverage" as "not addressing issues such as readability and other text qualities"), how useful or relevant is a surface-level representation of language, in general? The experiments these papers conduct achieve promising results - but is this merely because the kinds of texts they consider are very "plain" or fundamentally "surface-level" anyway? How do you think the methods described could be extended to apply to less straightforward text?

Sparck Jones: In order to develop effective procedures it is necessary to identify and respond to the context factors, i.e. input, purpose and output factors, that bear on summarising and its evaluation. (p. 1)

1 multi-document summarization and evaluation. 2 task characteristics input: a set of documents on...

Documents

focused summarization

focused u duc

multidocument u duc

time u duc

duc document

evaluation slide

np u ministers

theme u sentence extraction