PPT-Data Summarization Data summarization is either by;

1 Measurements of central tendency average measurements 2 Measurments of variability dispersion measurements Measures of Central Tendency What is central tendency The middle center of a variables distribution. video -Object driven Vs. Story driven. Presented By: Elad Osherov Jan 2013. Today’s talk. Motivation. Related Work. Object driven summarization. Story driven summarization. Results. Future Development. Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. 1982: -virus, 48,502 bp . 1995: h-influenzae, 1 Mbp . 2000: fly, 100 Mbp. 2001 – present. human (3Gbp), mouse (2.5Gbp), rat. *. , chicken, dog, chimpanzee, several fungal genomes. Gene Myers. Let’s sequence the human genome with the shotgun strategy. Lei Shi, Sibai Sun, . Yuan Xuan. , Yue Su, . Hanghang . Tong, Shuai Ma, Yang . Chen. Influence Graph. Initial. Tweet. Re-tweeting Graph. Re-tweets. Citing papers. Source. Paper. Paper Citation Graph. Date. ： . 2014/08/11. Author . ： . Lidan. . Shou. , . Zhenhua. Wang, . Ke. Chen, Gang Chen. Source. ： . SIGIR’13. Advisor: . Jia. -ling . Koh. Speaker. ： . Sz-Han,Wang. O. utline. Introduction. 1. Wan-Ting Hsu. National Tsing Hua University. Chieh. -Kai Lin. National Tsing Hua University. Project page. Outline. Motivation. Our Method. Training Procedures. Experiments and Results. Conclusion. Kathleen McKeown. Department of Computer Science. Columbia University. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. Text as input (one or more articles), paragraph summary as output. Document Summarization Abhirut Gupta Mandar Joshi Piyush Dungarwal Motivation The advent of WWW has created a large reservoir of data A short summary, which conveys the essence of the document, helps in finding relevant information quickly Ameet. Deshpande. March 24, 2020. TASK. Text Summarization is the reduction of data to a (minimal) subset which represents the original data. Two types of Summarization techniques. Extractive Summarization. Kathleen McKeown. Department of Computer Science. Columbia University. Today. HW3 assigned. Summarization (switch in order of topics). WEKA tutorial (for HW3). Midterms back. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization + DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction. Tal . Baumel. , Rafi Cohen, Michael Elhadad. Jan 2014. Generic Summarization. Generic Extractive Multi-doc Summarization:. Given a set of documents Di. Identify a set of sentences . Sj. . s.t.. |. Sj. Reddit. Posts. with Multi-level Memory Networks. . [. NAACL . 2019]. Group Presentation. WANG, Yue. 04/15/2019. Outline. Background. Dataset. Method. Experiment. Conclusion. 2. /16. Background. Challenge:. Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .