Conference Proceedings

Decoupling Encoder and Decoder Networks for Abstractive Document Summarization

Ying Xu, Jey Han Lau, Timothy Baldwin, Trevor Cohn

Proceedings of the Workshop on Summarization and Summary Evaluation Across Source Types and Genres, MultiLing@EACL 2017, Valencia, Spain, April 3, 2017 | Association for Computational Linguistics | Published : 2017

Abstract

Abstractive document summarization seeks to automatically generate a summary for a document, based on some abstract “understanding” of the original document. State-of-the-art techniques traditionally use attentive encoder–decoder architectures. However, due to the large number of parameters in these models, they require large training datasets and long training times. In this paper, we propose decoupling the encoder and decoder networks, and training them separately. We encode documents using an unsupervised document encoder, and then feed the document vector to a recurrent neural network decoder. With this decoupled architecture, we decrease the number of parameters in the decoder substant..

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