1. ClusterRank A Graph Based Method for Meeting Summarization Nikhil Garg School of Computer Science EPFL

4. eg. [5] This severe problem has made us look into other sources of energy

6. Random chit-chat

7. Redundancy

8. ASR errors

9. Dis-fluencies

16. Clustering based on word overlap

17. Proximity of text is important [1] A: Due to severity of this problem [5] A: This severe problem has made us look into other sources of energy [4] B: Oh. yeah, right. [3] A: Global Warming [2] B: Which problem ? window_above=3 window_below=2 High similarity merge_point (b) Merging multiple sentences (or clusters) above and below the merge point

18. ClusterRank Meeting Transcript Summary Clusters Cluster Graph Cluster Scores Sentence Scores The Big Picture Clustering Graph Construction Page Rank Sentence Scoring Greedy Selection

19. ClusterRank Meeting Transcript Summary Clusters Cluster Graph Cluster Scores Sentence Scores 0.6 0.7 0.1 0.2 0.9 C1 C2 C3 C5 C4 Clustering Graph Construction Page Rank Sentence Scoring Greedy Selection

20. ClusterRank Meeting Transcript Summary Clusters Cluster Graph Cluster Scores Sentence Scores Cluster Score(Si) = similarity(Si, centroid) * PR(cluster) Clustering Graph Construction Page Rank Sentence Scoring Greedy Selection

23. 137 Meeting transcripts

24. ROUGE [Lin 2004] evaluation scores

25. Summary length : 6% words

26. Comparison against human abstractive summaries * denotes significant improvement

31. Better clustering algorithm

32. Integrating user query

33. Use clusters for abstractive summarization

34. Thank You Questions?