ACL RD-TEC 1.0 Summarization of C02-1053
Paper Title:
EXTRACTING IMPORTANT SENTENCES WITH SUPPORT VECTOR MACHINES
EXTRACTING IMPORTANT SENTENCES WITH SUPPORT VECTOR MACHINES
Authors: Tsutomu Hirao and Hideki Isozaki and Eisaku Maeda and Yuji Matsumoto
Primarily assigned technology terms:
- algorithm
- automatic generation
- automatic text summarization
- boosting
- categorization
- chunking
- cross validation
- decision tree
- decision tree learning
- dependency structure analysis
- extraction technique
- five-fold cross validation
- inner product
- kernel
- language processing
- learning
- learning algorithm
- machine learning
- natural language processing
- parameter tuning
- polynomial kernel
- processing
- quadratic programming
- question answering
- question answering system
- ranking
- sentence extraction
- sentence ranking
- structure analysis
- summarization
- supervised learning
- supervised learning algorithm
- support vector machines
- svm-based important sentence extraction
- text categorization
- text summarization
- tree learning
- tuning
- validation
- weighting
Other assigned terms:
- approach
- case
- characters
- coherence
- data set
- dependency structure
- document
- experimental results
- feature
- fmeasure
- generation
- genre
- hypothesis
- key words
- linguistic
- measures
- method
- misclassification error
- natural language
- nouns
- null hypothesis
- occurrence probability
- paragraph
- parameter values
- part of speech
- positive and negative examples
- precision
- probability
- run-time
- sentence
- sentence position
- sentences
- statistical significance
- statistics
- style
- support vector
- svms
- technique
- technologies
- term
- term frequency
- test collection
- test data
- text
- text structure
- training
- training data
- training examples
- tree
- word
- words
