2002 SummarizationBeyondSentenceExtraction

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Subject Headings: Abstractional Multi-Document Summarization Algorithm.

Notes

  • This is an extended version of the paper (Knight & March, 2000) ⇒ Kevin Knight, and Daniel Marcu. (2000). “Statistics-based Summarization — Step One: Sentence Compression.” In: Proceedings of AAAI 2000 (AAAI 2000).

Cited By

Quotes

Author Keywords

Summarization; Compression; Noisy-channel model

Abstract

When humans produce summaries of documents, they do not simply extract sentences and concatenate them. Rather, they create new sentences that are grammatical, that cohere with one another, and that capture the most salient pieces of information in the original document. Given that large collections of text/abstract pairs are available online, it is now possible to envision algorithms that are trained to mimic this process. In this paper, we focus on sentence compression, a simpler version of this larger challenge. We aim to achieve two goals simultaneously: our compressions should be grammatical, and they should retain the most important pieces of information. These two goals can conflict. We devise both a and a decision-tree approach to the problem, and we evaluate results against manual compressions and a simple baseline.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2002 SummarizationBeyondSentenceExtractionDaniel Marcu
Kevin Knight		Summarization Beyond Sentence Extraction: A probabilistic approach to sentence compressionhttp://www.isi.edu/~marcu/papers/aij02-compression.pdf
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