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Neural sentence fusion for diversity driven abstractive multi-document summarization

机译:神经句融合,用于多样性驱动的抽象多文档摘要

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摘要

The lack of multi-document based models and the inaccuracy in representing multiple long documents into a fixed size vector inspired us to solve abstractive multi-document summarization. Also, there is lack of good multi-document based human-authored datasets to train any encoder-decoder models. To overcome this, we have designed complementary models for two different tasks such as sentence clustering and neural sentence fusion. In this work, we minimize the risk of producing incorrect fact by encoding a related set of sentences as an input to the encoder. We have applied our complementary models to implement a full abstractive multi-document summarization system which simultaneously considers importance, coverage, and diversity under a desired length limit. We conduct extensive experiments for all the proposed models which bring significant improvements over the state-of-the-art methods across different evaluation metrics. (C) 2019 Elsevier Ltd. All rights reserved.
机译:缺乏基于多文档的模型以及将多个长文档表示为固定大小的向量的不准确性,促使我们解决了抽象的多文档摘要。而且,缺乏良好的基于​​多文档的人类创作数据集来训练任何编码器-解码器模型。为了克服这个问题,我们为两个不同的任务(例如句子聚类和神经句子融合)设计了互补模型。在这项工作中,我们通过将一组相关的句子编码为编码器的输入,将产生不正确事实的风险降至最低。我们已经应用互补模型来实现一个完整的抽象多文档摘要系统,该系统同时在所需的长度限制内考虑重要性,覆盖范围和多样性。我们对所有提议的模型进行了广泛的实验,这些模型对不同评估指标上的最新方法进行了重大改进。 (C)2019 Elsevier Ltd.保留所有权利。

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