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Statistical NLP Spring 2011 Lecture 25: Summarization Dan Klein – UC Berkeley TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA
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Document Summarization
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Multi-document Summarization … 27,000+ more
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Extractive Summarization
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Selection Maximum Marginal Relevance mid-‘90s present Maximize similarity to the query Minimize redundancy [Carbonell and Goldstein, 1998] s1s1s1s1 s3s3s3s3 s2s2s2s2 s4s4s4s4 Q Greedy search over sentences
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Maximum Marginal Relevance Graph algorithms [Mihalcea 05++] mid-‘90s present Selection
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Maximum Marginal Relevance Graph algorithms mid-‘90s present s1s1s1s1 s3s3s3s3 s2s2s2s2 s4s4s4s4 Nodes are sentences Selection
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Maximum Marginal Relevance Graph algorithms mid-‘90s present s1s1s1s1 s3s3s3s3 s2s2s2s2 s4s4s4s4 Nodes are sentences Edges are similarities Selection
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Maximum Marginal Relevance Graph algorithms mid-‘90s present s1s1s1s1 s3s3s3s3 s2s2s2s2 s4s4s4s4 Nodes are sentences Edges are similarities Stationary distribution represents node centrality Selection
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Maximum Marginal Relevance Graph algorithms Word distribution models mid-‘90s present Input document distributionSummary distribution ~w P A (w) Obama? speech? health? Montana?w P D (w) Obama0.017 speech0.024 health0.009 Montana0.002 Selection
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Maximum Marginal Relevance Graph algorithms Word distribution models mid-‘90s present SumBasic [Nenkova and Vanderwende, 2005] Value(w i ) = P D (w i ) Value(s i ) = sum of its word values Choose s i with largest value Adjust P D (w) Repeat until length constraint Selection
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Maximum Marginal Relevance Graph algorithms Word distribution models Regression models mid-‘90s present s1s1s1s1 s2s2s2s2 s3s3s3s3 word values positionlength 12124 4214 6318 s2s2s2s2 s3s3s3s3 s1s1s1s1 F(x) frequency is just one of many features Selection
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Maximum Marginal Relevance Graph algorithms Word distribution models Regression models Topic model-based [Haghighi and Vanderwende, 2009] mid-‘90s present Selection
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H & V 09PYTHY
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Maximum Marginal Relevance Graph algorithms Word distribution models Regression models Topic models Globally optimal search mid-‘90s present [McDonald, 2007] s1s1s1s1 s3s3s3s3 s2s2s2s2 s4s4s4s4 Q Optimal search using MMR Integer Linear Program Selection
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[Gillick and Favre, 2008] Universal health care is a divisive issue. Obama addressed the House on Tuesday. President Obama remained calm. The health care bill is a major test for the Obama administration. s1s1s1s1 s2s2s2s2 s3s3s3s3 s4s4s4s4 conceptvalue Selection
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[Gillick and Favre, 2008] Universal health care is a divisive issue. Obama addressed the House on Tuesday. President Obama remained calm. The health care bill is a major test for the Obama administration. conceptvalue obama3 s1s1s1s1 s2s2s2s2 s3s3s3s3 s4s4s4s4 Selection
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[Gillick and Favre, 2008] Universal health care is a divisive issue. Obama addressed the House on Tuesday. President Obama remained calm. The health care bill is a major test for the Obama administration. conceptvalue obama3 health2 s1s1s1s1 s2s2s2s2 s3s3s3s3 s4s4s4s4 Selection
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[Gillick and Favre, 2008] Universal health care is a divisive issue. Obama addressed the House on Tuesday. President Obama remained calm. The health care bill is a major test for the Obama administration. conceptvalue obama3 health2 house1 s1s1s1s1 s2s2s2s2 s3s3s3s3 s4s4s4s4 Selection
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[Gillick and Favre, 2008] Universal health care is a divisive issue. Obama addressed the House on Tuesday. President Obama remained calm. conceptvalue obama3 health2 house1 s1s1s1s1 s2s2s2s2 s3s3s3s3 s4s4s4s4 The health care bill is a major test for the Obama administration.summarylengthvalue {s 1, s 3 }175 {s 2, s 3, s 4 }176 Length limit: 18 words greedy optimal Selection
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Maximize Concept Coverage [Gillick and Favre 09] Optimization problem: Set Coverage Value of concept c Set of concepts present in summary s Set of extractive summaries of document set D Results 23.5 35.0 4.00 6.85
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[Gillick, Riedhammer, Favre, Hakkani-Tur, 2008] total concept value summary length limit maintain consistency between selected sentences and concepts Integer Linear Program for the maximum coverage model Selection
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[Gillick and Favre, 2009] This ILP is tractable for reasonable problems Selection
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52 submissions 27 teams 44 topics 10 input docs 100 word summaries Gillick & Favre Rating scale: 1-10 Humans in [8.3, 9.3] Rating scale: 1-10 Humans in [8.5, 9.3] Rating scale: 0-1 Humans in [0.62, 0.77] Rating scale: 0-1 Humans in [0.11, 0.15] Results [G & F, 2009]
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[Gillick and Favre, 2008] Error Breakdown?
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How to include sentence position? First sentences are unique Selection
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Some interesting work on sentence ordering [Barzilay et. al., 1997; 2002] But choosing independent sentences is easier First sentences usually stand alone well Sentences without unresolved pronouns Classifier trained on OntoNotes: <10% error rate Baseline ordering module (chronological) is not obviously worse than anything fancier Selection
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Problems with Extraction It is therefore unsurprising that Lindsay pleaded not guilty yesterday afternoon to the charges filed against her, according to her publicist. What would a human do?
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Problems with Extraction It is therefore unsurprising that Lindsay pleaded not guilty yesterday afternoon to the charges filed against her, according to her publicist. What would a human do?
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Sentence Rewriting [Berg-Kirkpatrick, Gillick, and Klein 11]
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Sentence Rewriting [Berg-Kirkpatrick, Gillick, and Klein 11]
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Sentence Rewriting [Berg-Kirkpatrick, Gillick, and Klein 11]
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Sentence Rewriting [Berg-Kirkpatrick, Gillick, and Klein 11] New Optimization problem: Safe Deletions Set branch cut deletions made in creating summary s Value of deletion d How do we know how much a given deletion costs?
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Learning [Berg-Kirkpatrick, Gillick, and Klein 11] Features: Embed ILP in cutting plane algorithm. Results 41.3
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Sentence extraction is limiting... and boring! But abstractive summaries are much harder to generate… in 25 words? Beyond Extraction / Compression?
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http://www.rinkworks.com/bookaminute/
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Brown first saw the new planet Jan. 8 using the 48-inch Samuel Oschin Telescope Sample Errors:... news that the U.K. Panel on Takeover and Mergers... outside the U.S. Amoco already has shed such operations Preprocessing
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Sentence Boundary Detection (SBD) is non-trivial because periods are ambiguous (sentence boundaries, abbreviations, or both). Wall Street Journal Corpus
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Preprocessing Local context (one word on each side of the period) is sufficient. 80% of sentence-ending abbrs. in the WSJ test set Key innovations: Using an abbreviation list is too coarse.Abbr. Ends sentence TotalRatio Inc.1096830.16 Co.805660.14 Corp.676990.10 U.S.458000.06 Calif.24860.28 Ltd.231120.21
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