1. Finding Story Chains in Newswire Articles
Xianshu Zhu, Tim Oates
University of Maryland, Baltimore County
11/15/2018

2. Outline Introduction and Motivation Related Work
Random walk-based finding story chain algorithm
Experimental Result
Conclusion and Future Work
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3. Overview of O.J.Simpson Trial
Introduction
Story Chain: a set of events linking together.
Story: a set of events
Event: a news article
Overview of O.J.Simpson Trial
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4. Introduction
Information Overload !!!
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5. Introduction
Conventional search engine display unstructured search result
Search results ranked by relevance
Keyword-based ranking method
PageRank etc.
None of the ranking algorithm above can help to organize the search results by evolution of the story

6. Introduction Limitation of unstructured search results:
Missing the big picture on complex stories

7. Example: Hurricane Katrina
too much information. Don’t know what to look at
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8. Introduction Limitation of unstructured search results:
Search engines have little support for complex queries
Hard to find hidden relationships between two events
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9. Example: How O.J.Simpson trial relates to racial problem?
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10. Motivation
Can we build a search tool that can automatically find story chains given start and end articles as input?
An algorithm that can find out how two events are correlated by finding a chain of events that coherently connect them together?
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11. A Good Story Chain
Relevance: The articles on the chain should be relevant to the events connecting the two articles
Coherence: The transition between nodes on the chain should be smooth, with no concept jumping or jittering
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12. A Good Story Chain
Low redundancy
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13. A Good Story Chain
1994/6/12: Nicole Brown and Ronald Goldman are stabbed to death
1994/6/17: Simpson arrested for murder
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14. Related Work
Group articles into events according to the similarity of their contents and time stamps
Nallapati et al. “Event threading within news topics”, CIKM’04
Mei et al. “Discovering evolutionary theme patterns from text”, KDD’05
Little effort went into presenting data in a meaningful and coherence manner
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15. Related Work
Shahaf et al. connecting dots between news articles (KDD’10)
Chain coherence: determined by the weakest link on the chain
Finding story chain by a Linear Programming (LP) problem
Objective: Maximize the strength of the weakest link
Drawbacks:
(1) Not Efficient:
O(|D|) random walks to calculate word importance
The LP has O(|D|2|W|) variables
(2) Does not address Redundancy issue
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16. Our approach: Random walk based algorithm with pruning
Efficient
Low redundancy s t
Prune least relevant articles
Insert an article to the chain
Prune redundant articles
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17. Our Approach: Random walk based algorithm with pruning
Efficient
Low redundancy A s t
Prune least relevant articles
Insert an article to the chain
Prune redundant articles
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18. Our Approach: Random walk based algorithm with pruning
Efficient
Low redundancy B A s t C
Prune least relevant articles
Insert an article to the chain
Prune redundant articles
Stopping rule: no more articles can be added to the chain that increases link strength
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19. Compute link strength Problem with word-based similarity
Picking documents similar to s and t works well when s and t are close, but not for complex chains d3 d2 d1
Katrina
hurricane
tornado
storm
damage
Random walk approach can help to find relationship between documents that don’t have overlapped words
d1 and d2 are related: about the Katrina nature disaster
d2 is also about tornado and damage, but do not contain any words in d1
(a) d1 and d3 should be correlated
(b) random walk start from d1 can find the relationship
Nicole Brown and Ronald Goldman are stabbed to death
Jury panel selected: eight black, one white, one hispanic, two mixed race
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20. Compute link strength
Document-word bipartite graph: Document dj, which can be frequently reached from di, is highly related to di.
Documents
Words
.2/(.2+.2)=.5 d1 .2 w1
Random walk is used as a measure for document similarity. .7
.2/(.2+.2)=.5 .1 w2 d2 .4
.4/.4=1 .6
.7/( )=.33 .2 w3 d3 .8 w4
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21. Step 1: Prune least relevant articlesD
Apriori Principle: any subset of a frequent itemset must be frequent.
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22. Step 2: Insert an article to the chain
Goal: Find the best articles to be added to the link so as to improve the strength of that link
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23. Step 3: Prune redundant articles
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24. Step3: Prune redundant articles
Random walk starts from document nodes, will be more likely to reach articles that are in the same time bin and close in content
Hierarchical method to construct transition matrix t1 t2
time
0.5 1
Documents
Words w1 d1
0.2
0.7
0.1 w2 d2
0.4
0.6 w3
0.2 d3
0.8 w4
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25. Step 3: Prune redundant articles
Extended random walk formula:
Where is a vector indicating which nodes the random walk will jump to after a restart
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Documents
Words t1 t2
time
0.5 1
0.2
0.7
0.1
0.4
0.6
0.8
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26. Experimental Results Goal: Evaluation Method:
Can our algorithm produce good story chains efficiently?
How different pruning methods affect story chain construction and the efficiency of the algorithm
Evaluation Method:
Amazon’s Mechanical Turk (MTurk)
Workers rank the story chain from best (5) to worst (1) by relevance, coherence, coverage and redundancy

27. Experimental results News Stories: Data source: O.J.Simpson Trial
Hurricane Katrina
The earthquake in Japan 2011
Data source:
“North American News Text” from LDC (Linguistic Data Consortium)
“New York Times Annotated Corpus” from LDC
Crawl news articles from multiple sources (NYTime, Washington Post, Reuters, CNN etc.)
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28. Experimental results
Over articles contain key word “O.J. Simpson”
Start: O.J. Simpson’s Ex-Wife Found Dead in Double Homicide (06/13/94)
End: O.J. Simpson Jury Reaches Verdict (10/02/95)
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29. Experimental Results

30. Experimental results
Random walk without any pruning
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31. Experimental results
With two pruning methods
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32. Experimental Results

33. Conclusion and Future work
A random walk-based finding story chain algorithm
Experiments show that the algorithm can generate coherence story chains with no redundancy and with low computational complexity
Future work: detect and find story chains with different branches

34. Questions?
Thank you !!