Extractive text summarization has been an extensive research problem in the
field of natural language understanding. While the conventional approaches rely
mostly on manually compiled features to generate the summary, few attempts have
been made in developing data-driven systems for extractive summarization. To
this end, we present a fully data-driven end-to-end deep network which we call
as Hybrid MemNet for single document summarization task. The network learns the
continuous unified representation of a document before generating its summary.
It jointly captures local and global sentential information along with the
notion of summary worthy sentences. Experimental results on two different
corpora confirm that our model shows significant performance gains compared
with the state-of-the-art baselines.

本研究提出了一种称为混合记忆网络的全数据驱动端到端深度网络，用于单个文档摘要任务，其通过联合捕获局部和全局句子信息以及摘要有价值的句子的概念来学习文档的连续统一表示，实验结果表明，与现有最先进的基线相比，该模型展示了显著的性能提升。

基于混合记忆网络的文摘提取模型

Hybrid MemNet for Extractive Summarization

Single document summarization has enjoyed renewed interests in recent years
thanks to the popularity of neural network models and the availability of
large-scale datasets. In this paper we develop an unsupervised approach arguing
that it is unrealistic to expect large-scale and high-quality training data to
be available or created for different types of summaries, domains, or
languages. We revisit a popular graph-based ranking algorithm and modify how
node (aka sentence) centrality is computed in two ways: (a)~we employ BERT, a
state-of-the-art neural representation learning model to better capture
sentential meaning and (b)~we build graphs with directed edges arguing that the
contribution of any two nodes to their respective centrality is influenced by
their relative position in a document. Experimental results on three news
summarization datasets representative of different languages and writing styles
show that our approach outperforms strong baselines by a wide margin.

本文提出了一种针对新闻摘要自动提取的无监督方法，该方法采用神经网络模型并结合基于图论的排名算法，使用 BERT 模型来更好地捕获句子意义，显著超过强基线的效果。

无监督摘要的句子重要性再探讨

Sentence Centrality Revisited for Unsupervised Summarization

Till now, neural abstractive summarization methods have achieved great
success for single document summarization (SDS). However, due to the lack of
large scale multi-document summaries, such methods can be hardly applied to
multi-document summarization (MDS). In this paper, we investigate neural
abstractive methods for MDS by adapting a state-of-the-art neural abstractive
summarization model for SDS. We propose an approach to extend the neural
abstractive model trained on large scale SDS data to the MDS task. Our approach
only makes use of a small number of multi-document summaries for fine tuning.
Experimental results on two benchmark DUC datasets demonstrate that our
approach can outperform a variety of baseline neural models.

本文针对多文档自动摘要问题，提出了一个基于小型多文档训练集的神经抽象模型 fine-tuning 方法，实证结果在两个基准 DUC 数据集上表现优异。

基于神经网络的抽象式多文档摘要生成方法研究

Towards a Neural Network Approach to Abstractive Multi-Document  Summarization

Single document summarization is the task of producing a shorter version of a
document while preserving its principal information content. In this paper we
conceptualize extractive summarization as a sentence ranking task and propose a
novel training algorithm which globally optimizes the ROUGE evaluation metric
through a reinforcement learning objective. We use our algorithm to train a
neural summarization model on the CNN and DailyMail datasets and demonstrate
experimentally that it outperforms state-of-the-art extractive and abstractive
systems when evaluated automatically and by humans.

提出了一种用于单文档大意提取的全新算法，能够通过强化学习目标全局优化 ROUGE 评估指标，并在 CNN 和 DailyMail 数据集上进行了实验，展示它在人工和自动评估中均优于现有抽取和生成式提取系统的神经大意提取模型。