A Neural Attention Model for Sentence Summarization

Summarization based on text extraction is inherently limited, but generation-style ab-stractive methods have proven challeng-ing to build. In this work, we propose a fully data-driven approach to abstrac-tive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary con-ditioned on the input sentence. While the model is structurally simple, it can eas-ily 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. 1