A Neural Attention Model for Abstractive Sentence Summarization

Code

• github.com/Ganeshpadmanaban/Neural-Attention-Model
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• github.com/toru34/rush_emnlp_2015
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• github.com/Ganeshpadmanaban/Neural-Attention-Model-Abstractive-Summarization
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• github.com/tensorflow/models/tree/master/research/textsum
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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.

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