PPT-Big Data Text Summarization - 2017 Westminster Attack

CS4984CS5984 Final Presentation Team 4 Aaron Becker Colm Gallagher Jamie Dyer Jeanine Liebold Limin Yang Instructor Dr Edward A Fox Virginia Tech Blacksburg VA . Luka Totgergeli and. Benjamin Takač 5b. . Big Ben . is the nickname for the great bell of the clock at the north end of the . Palace of Westminster.. . The tower is now officially called the Elizabeth Towe. 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?. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. HEADLINE. Body. text,. body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text, body text. Julia Freeman and Souleiman Ayoub. Overview. Customizing a filled out template provided by Fusion. . Summarize news articles with attributes that are extracted. Named entities especially for organizations and persons. 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. 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. 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 Diversity driven Attention Model for Query-based Abstractive Summarization Preksha Nema *,  Mitesh Khapra *,  Anirban Laha* # ,   Balaraman Ravindran * * Indian Institute of Technology Madras, India (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization + DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction. Text 2. Text 3. Text 4. Text 5. Text 6. Text 7. Text 8. Text 9. Text 10. Text 11. Text 12. Text 13. Text 14. Text 15. Text 16. Text 17. Erbauer: . Max Mustermann (Ort). Bauzeit: xx Wochen. Steine: ca. 10.000. 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:. If you\'re looking to embark on a journey to master Big Data through Hadoop, the Hadoop Big Data course at H2KInfosys is your ideal destination. Let\'s explore why this course is your gateway to Big Data success.

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