1. PNFS: PERSONALIZED NEWS FILTERING & SUMMARIZATION ON THE WEB
Authors: Xindong Wu, Fei Xie, Gongqing Wu, Wei Ding 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2011
Presenter
Rashida Hasan
April 06, 2017
Center for Advanced Computer Studies
University of Louisiana at Lafayette

2. Overview Motivation Objective Paper Overview
Personalized News Filtering & Summarization (PNFS)
Experiments & Results
Related Work
Summary
Exam Questions
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University of Louisiana at Lafayette

3. Motivation
The indexed Web contains at least 5.02 billion pages (Tuesday, 08 September, 2015)
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University of Louisiana at Lafayette

4. Objectives
Recommendation, extraction, and summarization of interesting and useful information from web pages according to a user's personal preference.
Applications include public opinion investigation, intelligence gathering and monitoring, topic tracking and employment services.
Center for Advanced Computer Studies
University of Louisiana at Lafayette

5. Paper Overview A web news recommendation mechanism : A news filter :
Recommend interesting news to users
Obtain news from Google news (
A news filter :
Provide high quality news content for analyzing
An embedded learning component
Summarize web news :
Summarize useful and interesting information
Given in the form of keywords based on lexical chains
A keyword knowledge base is stored
A keyword extraction algorithm
Center for Advanced Computer Studies
University of Louisiana at Lafayette

6. Personalized News Filtering & Summarization (PNFS)
Personalized Web News Filtering
Web News Summarization
Keyword Knowledgebase
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University of Louisiana at Lafayette

7. PNFS System Architecture
PNFS system consists of two phases
Phase 1 : Personalized Web News Filtering
Phase 2 :Web News Summarization
Center for Advanced Computer Studies
University of Louisiana at Lafayette

8. PNFS System Architecture
Fig 1 : PNFS System Architecture
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University of Louisiana at Lafayette

9. PNFS System Architecture
PNFS system consists of two phases
Phase 1 : Personalized Web News Filtering
Filter out news stories uninteresting to the user
Filter out non news parts on news web pages
Phase 2 :Web News Summarization
Center for Advanced Computer Studies
University of Louisiana at Lafayette

10. System Architecture : Personalized Web News Filtering
The Internet
Web news
News Aggregator
News Filter
Recommended news
Learning Component
Keyword Knowledgebase
User Feedback
Center for Advanced Computer Studies
University of Louisiana at Lafayette

11. PNFS System Architecture
PNFS system consists of two phases
Phase 1 : Personalized Web News Filtering
Filter out news stories uninteresting to the user
Filter out non news parts on news web pages
Phase 2 :Web News Summarization
Gives a concise form of the news web page to the user that saves the reading time
The extracted keywords are also used to build a user interest model
Center for Advanced Computer Studies
University of Louisiana at Lafayette

12. System Architecture: Web News Summarization
Keyword Knowledge base
Lexical thesaurus
Word Segmentation
Filtered News
Compute Word Similarity and Co-Occurrence Frequency
Extract Candidate words
Construct Lexical Chains
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University of Louisiana at Lafayette

13. PNFS System Architecture
Fig 1 : PNFS System Architecture
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University of Louisiana at Lafayette

14. Personal Web News Recommendation
Feature Selection method to obtain the total word vocabulary
Recommendation algorithm to track the news event that the user would focus on based on the K-nearest neighbor algorithm
Probability model to recommend the topic interesting news using Naïve Bayes algorithm
Center for Advanced Computer Studies
University of Louisiana at Lafayette

15. Personal Web News Recommendation : Feature Selection
Feature Selection (D,k,m,n)
Input: D: document set of all categories; k: the number of features selected for each document; m: the number of topic categories; n: the number of features selected for each topic category
Output : F : selected feature set
For each category C
For each document d ϵ Ci
extract top k keywords from d
sort all words in according to the number of times they appear in the top k keyword lists
Fi=the n most frequent words in Ci
Return F= F1 ∪ F2 ∪…….. ∪ Fm
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16. Personal Web News Recommendation : Feature Selection
Most recent 5000 documents per category are collected for feature selection
Number of keywords extracted for each document is 50
Number of features selected for each topic category is 1000
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University of Louisiana at Lafayette

17. Personal Web News Recommendation : Feature Selection
The top 50 features selected for the technology topic category are as follows :
apple, company, google, iphone, user, mobile, vehicle, corp, software, app, video, technology, samsung, computer, internet, billion, safety, traffic, facebook, microsoft, credit, smartphone, service, ipad, version, price, web, office, honda, market, competitor, android, federal, gas, search, posted, major, executive, offline, site, incident, system, patent, security, youtube, device, model, industry, network, map
Center for Advanced Computer Studies
University of Louisiana at Lafayette

18. Personal Web News Recommendation
Feature Selection method to obtain the total word vocabulary
Recommendation algorithm to track the news event that the user would focus on based on the K-nearest neighbor algorithm
Probability model to recommend the topic interesting news using Naïve Bayes algorithm
Center for Advanced Computer Studies
University of Louisiana at Lafayette

19. Personal Web News Recommendation : Tracking News
After feature selection each news article is represented as a vector by the TFIDF term weighting scheme.
tf: the frequency of term ti in the given web page
N: the # of documents in the corpus
ni: the # of documents in the corpus that contain term ti
Center for Advanced Computer Studies
University of Louisiana at Lafayette

20. Personal Web News Recommendation : Tracking News
The cosine measure is used to compute the similarity of two vectors
Center for Advanced Computer Studies
University of Louisiana at Lafayette

21. Personal Web News Recommendation : Tracking News
Tracking_News (t1 , t2, k,n)
Input: t1 , t2 :similarity threshold (o.3, 0.6); k: the number of nearest neighbors (20);
Output : the top n tracking news stories and the candidate news for topic interesting news recommendation.
for each upcoming news story do
calculate the similarities of the news story with the users recently read stories and get k most nearest neighbors
if one of the k similarities is larger than t2
label the upcoming story as redundant;
continue;
if the average of the k similarities is larger than t1
put the new story into the tracking news queue;
if the average of the k similarities is less than t1
put the new story into the candidate news queue;
recommend the top n stories in the tracking news queue in the descending order of the average similarity.
Center for Advanced Computer Studies
University of Louisiana at Lafayette

22. Personal Web News Recommendation
Feature Selection method to obtain the total word vocabulary
Recommendation algorithm to track the news event that the user would focus on based on the K-nearest neighbor algorithm
Probability model to recommend the topic interesting news using Naïve Bayes algorithm
Center for Advanced Computer Studies
University of Louisiana at Lafayette

23. Personal Web News Recommendation: Interesting Topic News
K-nearest Neighbor performs well in tracking news events and finding novel news
Failed to reflect the diversity of user interest
Solution : Learning model based on Naïve Bayes
Naïve Bayes calculate the probability of a news story being interesting
Each news story is represented as feature-value vector
The Naïve Bayes classifier is built to calculate the topic distribution of user interests.
Center for Advanced Computer Studies
University of Louisiana at Lafayette

24. Personal Web News Recommendation: Interesting Topic News
The probability that document d is recommended to user u is computed as follows:
Center for Advanced Computer Studies
University of Louisiana at Lafayette

25. PNFS System Architecture
PNFS system consists of two phases
Phase 1 : Personalized Web News Filtering
Filter out news stories uninteresting to the user
Filter out non news parts on news web pages
Phase 2 :Web News Summarization
Gives a concise form of the news web page to the user that saves the reading time
The extracted keywords are also used to build a user interest model
Center for Advanced Computer Studies
University of Louisiana at Lafayette

26. Keyword Extraction Based on Lexical Chain (KLC)
Lexical Chain : A lexical chain is a sequence of words with related sense
The interpretation is composed of several disjoint lexical chains
All the possible interpretation form the interpretation space
The interpretation with the largest cohesion value represent the correct sense of the words in the text.
The cohesion value is defined by the sum of similarities between the words in lexical chains
Center for Advanced Computer Studies
University of Louisiana at Lafayette

27. Keyword Extraction Based on Lexical Chain (KLC)
Fig 2 :An example of resolving word sense ambiguity
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University of Louisiana at Lafayette

28. Keyword Extraction Based on Lexical Chain (KLC)
Word Co-occurrence Model :
Dice coefficient is used to compute the word relatedness degree.
Let x and y be two basic events in the probability space, representing the occurrence of words in a document.
The Dice coefficient is defined as
Center for Advanced Computer Studies
University of Louisiana at Lafayette

29. Keyword Extraction Based on Lexical Chain (KLC)
Keyword extraction algorithm
1. Words are segmented and stemmed, and stop words are removed
2. Select the top n words by TFIDF as candidate words
3. Build the disambiguation graph in which each node is a candidate word
4. Perform the word sense disambiguation for each candidate word
5. Build the actual lexical chains
6. Compute the weight of each candidate word wi as follows:
7. Select the top m words as the keywords extracted from the candidate words by their weights
Center for Advanced Computer Studies
University of Louisiana at Lafayette

30. Keyword Extraction Based on Lexical Chain (KLC)
n is 30 based on empirical studies
n should be between 20 and 50
If n is smaller than 20, the advantages of semantic relation would not be evident
If it is greater than 50, the importance of word frequency to the extracted keywords would be reduced
t3 is set to 0.3
Center for Advanced Computer Studies
University of Louisiana at Lafayette

31. Keyword Extraction Evaluation
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University of Louisiana at Lafayette

32. Experiments & Results:
Experiment 1: Impact of # of Keywords Selected
Fig 3 : The precision of KLC with three different feature sets
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University of Louisiana at Lafayette

33. Experiments & Results Experiment 1: Impact of # of Keywords Selected
Fig 4 : The recall of KLC with three different feature sets
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34. Experiments & Results Experiment 2: Nouns and Verbs
Fig 5 : The precision of KLC with two different candidate sets
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35. Experiments & Results Experiment 2: Nouns and Verbs
Fig 6 : The recall of KLC with two different candidate sets
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University of Louisiana at Lafayette

36. The Interface of the PNFS System
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37. The Interface of the PNFS System
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38. An Original Web News Example
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University of Louisiana at Lafayette

39. flood, home, end, year, overall, worst Australia, Queensland, state
Extracted Keywords :
flood, home, year, record, end, Australia, state, overall, Queensland, worst
Lexical chains :
flood, home, end, year, overall, worst
Australia, Queensland, state
record
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University of Louisiana at Lafayette

40. Related Work Recommender System Web News Extraction Keyword Extraction
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University of Louisiana at Lafayette

41. Related Work Recommender System Web News Extraction Keyword Extraction
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University of Louisiana at Lafayette

42. Related Work… Collaborativee Filtering Hybrid Recommendation
Recommender systems
Content Based Recommendation
Collaborativee Filtering
Hybrid Recommendation
Fig 7 : Different Techniques used in recommendation system
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University of Louisiana at Lafayette

43. Related Work Recommender System Web News Extraction Keyword Extraction
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University of Louisiana at Lafayette

44. Related Work… Web information extraction : Three Targets
Records in a web page
Specific interesting attributes
Main content of the page
Most web information exploration system use extraction rules
Extraction rules are represented by regular grammars, first order logic or a tag tree with features
Features include HTML tags, literal words, DOM tree paths, part-of-speech taggers, token lengths etc.
W4F:DOM tree path to address a web page
Chakrabarti uses URL tokens, HTML titles and keywords
NFAS: Filtering and summarizing
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University of Louisiana at Lafayette

45. Related Work Recommender System Web News Extraction Keyword Extraction
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University of Louisiana at Lafayette

46. Related Work Keyword Extraction
Supervised extraction
Unsupervised extraction
Supervised extraction : GenEX, Kea System
Unsupervised extraction: Graph based ranking method, candidate phrase generation method, lexical chain based method
Center for Advanced Computer Studies
University of Louisiana at Lafayette

47. Summary
Designed a content-based news recommender using two learning strategies to model the user interest preference
A news filter is used to filter out the advertisements and other irrelevant parts on the news Web page
Presented a new keyword extraction method based on semantic relations
Studied the semantic relations between words based on lexical thesaurus and word co-occurrence
Constructed lexical chains to link the relations and extract keywords
Center for Advanced Computer Studies
University of Louisiana at Lafayette

48. Exam Questions Q1: What are the Application of PNFS
Public opinion investigation
Intelligence gathering and monitoring
Topic tracking
Employment Services
Q2: What are the components for Personalized Filtering Subsystem ?
A news aggregator
A news filter
A learning component
A Keyword knowledge base
Center for Advanced Computer Studies
University of Louisiana at Lafayette

49. Exam Questions
Q3: How to represent news article by TFIDF term weighting scheme ?
Answer:
tf: the frequency of term ti in the given web page
N: the # of documents in the corpus
ni: the # of documents in the corpus that contain term ti
Center for Advanced Computer Studies
University of Louisiana at Lafayette

50. Thank You !! Questions ? Center for Advanced Computer Studies
University of Louisiana at Lafayette