A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush,Sumit Chopra,Jason Weston +2 more
- pp 379-389
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TLDR
The authors propose a local attention-based model that generates each word of the summary conditioned on the input sentence, which shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.Abstract:
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.read more
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