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Article overview
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DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization | Jiaxin Shi
; Chen Liang
; Lei Hou
; Juanzi Li
; Zhiyuan Liu
; Hanwang Zhang
; | Date: |
7 Nov 2018 | Abstract: | We propose DeepChannel, a robust, data-efficient, and interpretable neural
model for extractive document summarization. Given any document-summary pair,
we estimate a salience score, which is modeled using an attention-based deep
neural network, to represent the salience degree of the summary for yielding
the document. We devise a contrastive training strategy to learn the salience
estimation network, and then use the learned salience score as a guide and
iteratively extract the most salient sentences from the document as our
generated summary. In experiments, our model not only achieves state-of-the-art
ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the
out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1
F-1 score of 39.41 on CNN/Daily Mail test set with merely $1 / 100$ training
set, demonstrating a tremendous data efficiency. | Source: | arXiv, 1811.2394 | Services: | Forum | Review | PDF | Favorites |
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