In this paper, we propose the concept of summary prior to define how much a sentence is appropriate to be selected into summary without consideration of its context. Different from previous work using manually compiled documentindependent features, we develop a novel summary system called PriorSum, which applies the enhanced convolutional neural networks to capture the summary prior features derived from length-variable phrases. Under a regression framework, the learned prior features are concatenated with document-dependent features for sentence ranking. Experiments on the DUC generic summarization benchmarks show that PriorSum can discover different aspects supporting the summary prior and outperform state-of-the-art baselines.
Learning Summary Prior Representation for Extractive Summarization
Ziqiang Cao,Furu Wei,Sujian Li,Wenjie Li,M. Zhou,Houfeng Wang
Published 2015 in Annual Meeting of the Association for Computational Linguistics
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- Publication year
2015
- Venue
Annual Meeting of the Association for Computational Linguistics
- Publication date
2015-07-01
- Fields of study
Computer Science
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