ACL RD-TEC 1.0 Summarization of N04-1015

Paper Title:
CATCHING THE DRIFT: PROBABILISTIC CONTENT MODELS, WITH APPLICATIONS TO GENERATION AND SUMMARIZATION

Authors: Regina Barzilay and Lillian Lee

Other assigned terms:

  • american news corpus
  • approach
  • bigram
  • bigram language model
  • binary classification problem
  • classification problem
  • cluster
  • clusters
  • cognitive
  • comprehension
  • computational models
  • concreteness
  • corpora
  • correlation
  • data sparseness
  • development set
  • discourse
  • distribution
  • distributional information
  • document
  • document collections
  • document content
  • document structure
  • domain knowledge
  • domain-specific knowledge
  • empirical results
  • estimation
  • evaluation metric
  • evaluations
  • fact
  • feature
  • feature set
  • formalism
  • formalisms
  • generation
  • grid
  • hypothesis
  • implementation
  • input text
  • knowledge
  • knowledge base
  • language model
  • language models
  • language-modeling research
  • lexical similarity
  • linguistic
  • linguistic information
  • markov models
  • measure
  • measures
  • method
  • model parameters
  • model size
  • names
  • natural language
  • news corpus
  • paragraph
  • paragraphs
  • parallel corpus
  • parameter values
  • permutation
  • priori
  • probabilities
  • probability
  • probability estimates
  • procedure
  • process
  • proper names
  • relation
  • representations
  • rhetorical relations
  • runtime
  • schema
  • sentence
  • sentence similarity
  • sentences
  • state-specific language model
  • stems
  • summarization task
  • system performance
  • technique
  • term
  • terms
  • text
  • text structure
  • tokens
  • topics
  • training
  • training corpora
  • training data
  • training set
  • transition probabilities
  • understanding
  • vocabulary
  • vocabulary size
  • word
  • word distribution
  • word sequence
  • words

Extracted Section Types:


This page last edited on 10 May 2017.

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