An Attention-Based Syntax-Tree and Tree-LSTM Model for Sentence Summarization
Wenfeng Liu · International Journal of Performability Engineering · 2017
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.