1. AUTOMATIC DOCUMENT SUMMARIZATION
FINDWISE

2. Single document summarization
Proposed use for Findwise:
• Meta data for indexing service
Unsupervised:
• No need for trainingset
• Relative domain independence
• Relative language independence

3. Preprocessing
Mandatory Additional
• Sentence splitting • Named Entity Recognition
• Tokenization • Keyword extraction
• Stemming • tfidf term weighting
• PoS-tagging

4. Sentence extraction
Sentence ranking
• Real value ranking
• Relevance ordering
Sentence selection
• Desired summary length
Sentence ordering
• Final presentation

5. TextRank
Graph based
• Sentences as vertices
• Similarity as edges
Iterative ranking
• PageRank

6. Sentence Similarity
What makes two sentences similar?
Explored variations
• Shared words
• Word importance
• Lexical filtering
• Length normalization
• Advanced analysis

7. K-means clustering
Approach:
• Sentences as points
• Divide into clusters
• Select sentences from each cluster
• Diverse summaries

8. Domain customization
Domain: short news articles in English
• Sentence position important
• Use domain knowledge to improve performance
• Other boosting for other domains

9. Multi document summarization
Sentence Ranking Sentence selection
• TextRank • Similarity threshold
• K-Means clustering

10. Sentence Ordering
Paragraph selection Paragraph merging
• Topical closeness • Date of publication
• Sentence Similarity • Original position

11. Results single document
Algorithm ROUGE
Ngram(1,1)
TextRank 0.4797
K-means 0.4680
One-class SVM 0.4343
TextRank 0.4708
Original
K-means Original 0.4791
Baseline 1 0.4649
Baseline 2 0.3998

12. Results multi document
Algorithm ROUGE
Ngram(1,1)
TextRank 0.2537
K-means 0.2400
MetaRank 0.2561
Baseline 1 0.2317
Baseline 2 0.2054