Most extractive summarization methods focus on the main body of the document
from which sentences need to be extracted. However, the gist of the document
may lie in side information, such as the title and image captions which are
often available for newswire articles. We propose to explore side information
in the context of single-document extractive summarization. We develop a
framework for single-document summarization composed of a hierarchical document
encoder and an attention-based extractor with attention over side information.
We evaluate our model on a large scale news dataset. We show that extractive
summarization with side information consistently outperforms its counterpart
that does not use any side information, in terms of both informativeness and
fluency.

本文提出了利用辅助信息进行抽取式文本摘要的框架，该框架包括分层文档编码器和基于注意力机制的摘要提取器，利用大规模新闻数据集证明了与不使用辅助信息相比，利用辅助信息进行抽取式文本摘要可以更加有效地提高信息量和流畅度。

具有侧面信息的神经抽取式摘要生成

Neural Extractive Summarization with Side Information

Traditional approaches to extractive summarization rely heavily on
human-engineered features. In this work we propose a data-driven approach based
on neural networks and continuous sentence features. We develop a general
framework for single-document summarization composed of a hierarchical document
encoder and an attention-based extractor. This architecture allows us to
develop different classes of summarization models which can extract sentences
or words. We train our models on large scale corpora containing hundreds of
thousands of document-summary pairs. Experimental results on two summarization
datasets demonstrate that our models obtain results comparable to the state of
the art without any access to linguistic annotation.

论文提出了一种基于神经网络和连续句子特征的数据驱动型抽取式摘要方法，采用层级文档编码器和基于注意力机制的提取器的通用框架，能够训练不同的摘要模型（提取句子或单词），在大规模语料库上进行实验结果表明，该方法在不需要语言注解的情况下取得了与现有技术相当的效果。