'Text summarization' diaporamas de présentation

Automatic Text Summarization Introduction and Research Problems

Automatic Text Summarization Introduction and Research Problems. Talk Outline. Why automatic text summarization? What is automatic text summarization? Classification of methodologies. Multiple Document Summarization. Evaluation. Research Problems. Demo. Why Summarization?.

1.42k views • 104 slides

Text Summarization: News and Beyond

Text Summarization: News and Beyond. 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

982 views • 97 slides

Text Summarization

2.72k views • 81 slides

Genetic Programming Applied to Natural Language Processing

Genetic Programming Applied to Natural Language Processing. Dr. Nguyen Quang Uy Faculty of Information Technology Military Technical Academy Hanoi, Vietnam. Outline. An Introduction to Genetic Programming Genetic Programming Applied to Natural Language Processing Text Summarization

996 views • 73 slides

Summarization Technologies and Evaluation ?????????

Summarization Technologies and Evaluation ?????????. Hsin-Hsi Chen Department of Computer Science and Information Engineering National Taiwan University ??? ????????????. Outlines. Introduction Architecture of a Summarization System An Evaluation Model using Question Answering System

935 views • 72 slides

Modern Information Retrieval

Modern Information Retrieval. Lecture 1: Introduction. Lecture Overview. Introduction to the Course Introduction to Information Retrieval The Information Seeking Process Information Retrieval History and Developments Discussion References. Lecture Overview. Introduction to the Course

621 views • 43 slides

Automatic Text Summarization

643 views • 38 slides

Automatic text summarization

Automatic text summarization. Hercules Dalianis NADA-KTH Royal Institute of Technology 100 44 Stockholm ph: +46-8-790 91 05 mobile: +46 70 568 13 59 email: hercules@kth.se. Overview of talk. Background Other summarizers Technique Future improvements Applications Evaluation.

570 views • 32 slides

Processing of large document collections

423 views • 30 slides

Natural Language Processing

Natural Language Processing. Rada Mihalcea. Fall 2008. Any Light at The End of The Tunnel ?. Yahoo, Google, Microsoft ? Information Retrieval Monster.com, HotJobs.com (Job finders) ? Information Extraction + Information Retrieval Systran powers Babelfish ? Machine Translation

459 views • 27 slides

Kathy McCoy

272 views • 26 slides

A Survey on Automatic Text/Speech Summarization

A Survey on Automatic Text/Speech Summarization. Shih-Hsiang Lin( ??? ) Department of Computer Science & Information Engineering National Taiwan Normal University. References: D, Das and A. F. T. Martins, A Survey on Automatic Text Summarization , 2007

271 views • 25 slides

Automatic Text Summarization

492 views • 24 slides

Some extra stuff

Some extra stuff. Semantic change that results in an antonym of the original word: awful : original meaning: 'awe-inspiring, filling (someone) with deep awe', as in the awful majesty of the Creator; new meaning: 'breath- takingly bad; so bad that it fills (a person) with awe and amazement'

399 views • 22 slides

LING/C SC/PSYC 438/538

307 views • 20 slides

Processing of large document collections

Processing of large document collections. Part 1 (Introduction) Helena Ahonen-Myka Spring 2006. 1. Introduction. course organization introduction to the topic applications methods learning goals schedule. Organization of the course. lectures (Helena Ahonen-Myka)

306 views • 20 slides

Processing of large document collections

Processing of large document collections. Part 1 (Introduction) Helena Ahonen-Myka Spring 2006. 1. Introduction. course organization introduction to the topic applications methods learning goals schedule. Organization of the course. lectures (Helena Ahonen-Myka)

297 views • 20 slides

Biased LexRank : Passage Retrieval using Random Walks with Question-Based Priors

Biased LexRank : Passage Retrieval using Random Walks with Question-Based Priors. Presenter : JHOU, YU-LIANG Authors : Jahna Otterbacher a , Gunes Erkan b , Dragomir R. Radev 2009, IPM. Outlines. Motivation Objectives Methodology Experimental Result Conclusions Comments.

330 views • 16 slides

S ommarizzazione e G enerazione

214 views • 13 slides

DivRank: Interplay of Prestige and Diversity in Information Networks

DivRank: Interplay of Prestige and Diversity in Information Networks. Qiaozhu Mei 1,2 , Jian Guo 3 , Dragomir Radev 1,2 1. School of Information 2. Computer Science and Engineering 3. Department of Statistics University of Michigan. Diversity in Ranking.

395 views • 11 slides