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EMNLP
2007

Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources

9 years 2 months ago
Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources
We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its importance in the document. We apply novel features based on news search query logs and Wikipedia entities. Using the RankNet learning algorithm, we train a pair-based sentence ranker to score every sentence in the document and identify the most important sentences. We apply our system to documents gathered from CNN.com, where each document includes highlights and an article. Our system significantly outperforms the standard baseline in the ROUGE-1 measure on over 70% of our document set.
Krysta Marie Svore, Lucy Vanderwende, Christopher
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Krysta Marie Svore, Lucy Vanderwende, Christopher J. C. Burges
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