PPT-Summarization:

Overview Ling573 Systems amp Applications March 31 2016 Roadmap Dimensions of the problem Architecture of a Summarization system Summarization and resources Evaluation Logistics Check. chua2009smuedusg Sitaram Asur Social Computing Lab visited Hewlett Packard Research Labs Palo Alto California USA sitaramasurhpcom ABSTRACT Social media services such as Twitter generate phenome nal volume of content for most realworld events on a da edu ChengXiang Zhai Department of Computer Science University of Illinois at UrbanaChampaign Urbana IL 61801 czhaicsuiucedu Neel Sundaresan eBay Research Labs 2145 Hamilton Avenue San Jose CA 95125 nsundaresanebaycom ABSTRACT Web 20 technologies have Course: QP Reference ID TEL/N2203, TEL/N2204, TEL/N2205 Name of Training Provider Aegis Knowledge Trust Total Duration of Training In Days 30 In Hours 140 Training Proportion Theory (%) 30 Practical 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?. By . : . asef. . poormasoomi. Supervisor. : Dr. . Kahani. autumn 2010. Ferdowsi. University of . Mashad. Introduction. summary. : . brief. but . accurate. representation of the . contents. of a document. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. 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. Abigail See, Peter J. Liu, Christopher D. Manning. Presented by: Matan . Eyal. Agenda. Introduction. Word Embeddings. RNNs. Sequence-to-Sequence. Attention. Pointer Networks. Coverage Mechanism. Introduction . 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 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:. 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 variable’s distribution..