1. Graph-based Text Summarization
Lin Ziheng
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2. Aims
Build a graph that models the development (for writers) and consumption (for readers) of ideas in text through time
Use rhetorical relations to help in recognizing the important sentences in text
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3. Random Walk Depends on current state Convergence Google PageRank:1 2 3
Depends on current state
Convergence
Google PageRank: 4 5
0<d<1, usually d = 0.85
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4. Citation Network New papers can cite old papers
Old papers are not updated
New paper
Old papers
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5. The Internet
A new page must have at least one incoming link, may link to existing pages
Old pages can update their links
New page
Old pages
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6. Graph-based summarization: LexRank
Nodes = sentences
Edges = cosine similarity
Fully connected
Undirected
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7. Graph-based summarization: TextRank
Nodes = sentences
Edges = similarity
Backward links
Directed s1 s4 s2
New sentence
Old sentences s3
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8. Writing/Reading Process
Assumption
Readers read from the beginning towards the end
Writers write from the beginning towards the end
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9. Blog Network
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10. Building Graph
Out degree: prop. to how long the sent. stays in the graph (e.g., 1st:3, 2nd:2, 3rd:1)
In degree: importance
Edges: cosine, co-occurrence, longest common subsequence, etc..
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11. doc1
doc2
doc3
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12. Sentence Extraction In degree Run PageRank Unbiased
Biased towards query
d1s1: 2 d2s1: 3 d3s1: 3
d1s2: 1 d2s2: 4 d3s2: 0
d1s3: 4 d2s3: 1 d3s3: 0
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13. Evaluation 1 Dataset: Duc’04 task 2 NUS WING Group Meeting in degree
pagerank
LexRank
t = 1
t = 0.9
t = 0.7
t = 0.5
t = 0.3
t = 0.2
t = 0.1
node start
rank=1
rank=cosine
ROUGE-1 R avg
ROUGE-1 P avg
ROUGE-1 F avg
ROUGE-2 R avg
ROUGE-2 P avg
ROUGE-2 F avg
ROUGE-L R avg
ROUGE-L P avg
ROUGE-L F avg
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14. Evaluation 2 Dataset: Duc’06 Unbiased / Biased Rearranging doc length
# outlinks per sent per timestep
ROUGE-2
ROUGE-SU4
Unbiased no 1
yes 2 5 10
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15. Conclusion from Evaluation 2
Duc’06 is query-based, so biased PageRank gives better results
Rearranging doc length is not necessary if there is no extremely long document in the cluster
#outlinks is important, different #outlinks gives different inlink density.
We need to look at how the dimension of the graph (D * L) is related to the inlink density
F(D, L) => #outlinks
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