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arXiv:1509.00685cs.CL

A Neural Attention Model for Abstractive Sentence Summarization

Alexander M. Rush, Sumit Chopra, Jason Weston

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.

arXiv comments
Proceedings of EMNLP 2015
Journal reference
DOI
Subjects
cs.CLComputation and Language cs.AIArtificial Intelligence

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  1. Ask HN: How far are we with abstractive text summarization? 1 comment

    • Sequence-to-sequence attention models: https://github.com/tensorflow/models/tree/master/textsum https://arxiv.org/abs/1509.00685

      guard0gView on HNParent context

  2. Text Summarization with TensorFlow 1 comment

    • The code can be used to train on other data. All you really need is a collection of news articles. I think there are some free ones available. This dataset was only used to benchmark against other published results. It was first proposed in https://arxiv.org/abs/1509.00685.

      peterjliuView on HNParent context

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