Abstractive summarization has been studied using neural sequence transduction
methods with datasets of large, paired document-summary examples. However, such
datasets are rare and the models trained from them do not generalize to other
domains. Recently, some progress has been made in learning sequence-to-sequence
mappings with only unpaired examples. In our work, we consider the setting
where there are only documents (product or business reviews) with no summaries
provided, and propose an end-to-end, neural model architecture to perform
unsupervised abstractive summarization. Our proposed model consists of an
auto-encoder where the mean of the representations of the input reviews decodes
to a reasonable summary-review while not relying on any review-specific
features. We consider variants of the proposed architecture and perform an
ablation study to show the importance of specific components. We show through
automated metrics and human evaluation that the generated summaries are highly
abstractive, fluent, relevant, and representative of the average sentiment of
the input reviews. Finally, we collect a reference evaluation dataset and show
that our model outperforms a strong extractive baseline.

本文提出了一种基于神经网络的无监督自动编码器架构，用于进行商品评论的抽象摘要，通过自动化指标和人类评估表明，该模型生成的摘要高度抽象、流畅、相关，并代表了输入评论的情感倾向。

MeanSum: 一种用于无监督多文档抽象摘要的神经模型

MeanSum: A Neural Model for Unsupervised Multi-document Abstractive  Summarization

There are three modalities in the reading comprehension setting: question,
answer and context. The task of question answering or question generation aims
to infer an answer or a question when given the counterpart based on context.
We present a novel two-way neural sequence transduction model that connects
three modalities, allowing it to learn two tasks simultaneously and mutually
benefit one another. During training, the model receives
question-context-answer triplets as input and captures the cross-modal
interaction via a hierarchical attention process. Unlike previous joint
learning paradigms that leverage the duality of question generation and
question answering at data level, we solve such dual tasks at the architecture
level by mirroring the network structure and partially sharing components at
different layers. This enables the knowledge to be transferred from one task to
another, helping the model to find a general representation for each modality.
The evaluation on four public datasets shows that our dual-learning model
outperforms the mono-learning counterpart as well as the state-of-the-art joint
models on both question answering and question generation tasks.

该论文提出了一种新颖的双向神经序列转换模型，能够同时学习阅读理解中的三种知识输入方式，即问题、答案和上下文，并通过分层注意力过程对不同形态之间的交互信息进行捕捉和建模。该模型在四个公开数据集上的表现优于其他同类神经网络模型，尤其在问题回答和生成双方面具有更广泛的应用前景。