Supervised and Unsupervised Learning for Sentence Compression

In Statistics-Based Summarization - Step One: Sentence Compression, Knight and Marcu (Knight and Marcu, 2000) (KM Knight and Marcu use a corpus of 1035 training sentences. More data is not easily available, so in addition to improving the original K&M noisy-channel model, we create unsupervised and semi-supervised models of the task. Finally, we point out problems with modeling the task in this way. They suggest areas for future research.

• Publication year

  2005

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  2005-06-25

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/1219840.1219876
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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