PPT-Concept based Multi-Document Text Summarization

Author : giovanna-bartolotta | Published Date : 2017-04-28

By asef poormasoomi Supervisor Dr Kahani autumn 2010 Ferdowsi University of Mashad Introduction summary brief but accurate representation of the contents

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Concept based Multi-Document Text Summarization: Transcript


By asef poormasoomi Supervisor Dr Kahani autumn 2010 Ferdowsi University of Mashad Introduction summary brief but accurate representation of the contents of a document. 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. Overview. Ling573. Systems & Applications. March 31. , 2016. Roadmap. Dimensions . of the problem. Architecture . of a Summarization system. Summarization and resources. Evaluation. Logistics Check-. Reviews & Speech. Ling 573. Systems and Applications. May . 26, 2016. Roadmap. Abstractive summarization example. Using Abstract Meaning Representation. Review . summarization:. Basic approach. Learning what users want. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. 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. and Organization for User-content Interaction. References. : 1. “Spoken Document Understanding and Organization”, IEEE Signal . Processing Magazine, Sept. 2005, Special Issue on Speech Technology. 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 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. 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. 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..

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