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An Attention-Based Syntax-Tree and Tree-LSTM Model for Sentence Summarization

Volume 13, Number 5, September 2017 - Paper 20  - pp. 775-782
DOI: 10.23940/ijpe.17.05.p20.775782

Wenfeng Liua,b, Peiyu Liua,c,*, Yuzhen Yangb, Yaling Gaod, Jing Yia,e

aSchool of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China
Department of Computer and Information Engineering, Heze University, Heze, 274015, China
Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan,250014, China
Ruipu Peony Industrial Technology Development Co., Ltd, 274015, China
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China

(Submitted on January 29, 2017; Revised on April 12, 2017; Accepted on June 23, 20177)


Generative Summarization is of great importance in understanding large-scale textual data. In this work, we propose an attention-based Tree-LSTM model for sentence summarization, which utilizes an attention-based syntactic structure as auxiliary information. Thereinto, block-alignment is used to align the input and output syntax blocks, while inter-alignment is used for alignment of words within that of block pairs. To some extent, block-alignment can prevent structural deviations on the long sentences and inter-alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end-to-end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state-of-art on DUC-2004 shared task.


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