1. Mutual-reinforcement document summarization using embedded graph based sentence clustering for storytelling Zhengchen Zhang , Shuzhi Sam Ge , Hongsheng He IPM2012
Hao-Chin Chang
Department of Computer Science & Information Engineering
National Taiwan Normal University
2011/03/09

2. Outline
Introduction
Sentence ranking using embedded graph based sentence clustering
Document modeling
Embedded graph based sentence clustering
Mutual-reinforcement ranking algorithm
Experiment
Conclusion and Future work 橘

3. Introduction There are three phrases in the framework
Document modeling
document is modeled by a weighted graph with vertexes that represent sentences of the document
Sentence clustering
The sentences are clustered into different groups to find the latent topics in the story. To alleviate the influence of unrelated sentences in clustering,
Sentence ranking
An embedding process is employed to optimize the document model
We propose a framework which considers the mutual effect between clusters, sentences and terms instead of the relationship between documents, sentences, and terms to employ the cluster level information

4. Introduction
The sentences are then clustered into different groups according to the distance between two sentences
In order to alleviate the influence of unrelated sentences in sentence clustering, we employ an embedding process to optimize the graph vertexes
The contributions of this paper are summarized
An embedded graph based sentence clustering method is proposed for sentence grouping of a document, which is robust with respect to different cluster numbers
An iterative ranking method is presented which considers the mutual-reinforcement between terms, sentences and sentence clusters
A document summarization framework considering sentence cluster information is proposed and the framework is evaluated using DUC data sets.

5. Sentence ranking using embedded graph based sentence clustering
To reduce the influence of the low cosine similarity weights and to enhance sentence clustering performance, an embedding algorithm is performed on the graph
the sentences are ranked according to the mutual effects between sentences, termsand clusters based on the assumptions
A sentence is assigned a high rank if it is similar to many high ranking clusters and it contains many high ranking terms
The rank of a cluster is high if it contains many high ranking sentences and many high ranking terms
The rank of a term is high if it appears in many high ranking sentences and clusters.

6. Document modeling
The weight wij of an edge denotes the distance between sentences si and sj which is a cosine similarity between the vectors of two sentences

7. Embedded graph based sentence clustering
To alleviate the influence of unrelated sentences, we embed the original matrix D of a document into lower dimension space inspired by Locally Linear Embedding (LLE)
A sentence di which is a column vector of D is expressed as a linear combination of its ni most similar sentences dj
where i is the set of sentences most similar to di

8. Embedded graph based sentence clustering
In graph embedding , we minimize the following cost function of approximation error to determine the optimal weight matrix rij
Wi =[ri1, …, rin] are the weights connecting di to its neighbors
Partial derivatives with respect to each weight rij
Wi is found by solving the equations

9. Embedded graph based sentence clustering
The vectors of embedded sentences with enhanced relationship di are obtained by minimizing the cost function
While the embedding operation keeps the relationship between a sentence and a set of neighbors
The performance of embedding operation will improve if there are more points in the graph

10. Mutual-reinforcement ranking algorithm
To employ the cluster-level information and the latent theme information in the clusters for document summarization
D is the sentence–term matrix of the document
where cluster cj is represented by a vector which is summary of the vectors of all the sentences in this cluster
The weight of the edge connecting term tl and cluster cj

11. Mutual-reinforcement ranking algorithm
r(si) is the rank of sentencesi, r(cj) is the rank of cluster cj, and r(tl) is the rank of term tl
The sentence with the highest score is selected as a part of the summarization

12. Experiment

13. Experiment KM and agglomerative (AGG) clustering
MREG algorithm which is named KM-TSC-EM
mutual-reinforcement between sentences and clusters (KM-SC)
TSC is short for Term, Sentence and Cluster
EM is short for Embedded Graph based sentence clustering

14. Experiment a = 0.25 b = 0.25 and c = 0.50 a = 0.50, b = 0.50,c = 1.0
a = 0.750, b = 1.0,c = ROUGE-1 scores
a = 0.750, b = 1.0,c = ROUGE-1 scores

15. Conclusion
The sentences were clustered into different groups, and an embedding process was employed to reduce the effect of unrelated sentences and to enhance the sentence clustering performance.
Performance comparison of different combinations of components illustrated that the algorithm improved system performance and was more robust with respect to different cluster numbers.
Computer Speech & Language Camille Guinaudeau , Guillaume Gravier , Pascale Sébillo Enhancing lexical cohesion measure with confidence measures, semantic relations and language model interpolation for multimedia spoken content topic segmentation
Confidence score

16. 摘要示意圖 訓練文本語料 相關特徵 詞頻特徵 上下文特徵 一般化 收集 生成摘要 語料庫 KL(D||S) 初次檢索系統KL(S||D)… Sn
訓練文本語料
初次檢索系統KL(S||D)
相關特徵
詞頻特徵
上下文特徵
一般化 收集
語料庫
生成摘要
KL(D||S)