A Neural Attention Model for Abstractive Sentence Summarization
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.
EMNLP