Sentence summarization shortens given texts while maintaining core contents
of the texts. Unsupervised approaches have been studied to summarize texts
without human-written summaries. However, recent unsupervised models are
extractive, which remove words from texts and thus they are less flexible than
abstractive summarization. In this work, we devise an abstractive model based
on reinforcement learning without ground-truth summaries. We formulate the
unsupervised summarization based on the Markov decision process with rewards
representing the summary quality. To further enhance the summary quality, we
develop a multi-summary learning mechanism that generates multiple summaries
with varying lengths for a given text, while making the summaries mutually
enhance each other. Experimental results show that the proposed model
substantially outperforms both abstractive and extractive models, yet
frequently generating new words not contained in input texts.

本篇论文提出了一种基于强化学习的抽象模型，用于无人工摘要的句子摘要，同时还开发了一种多摘要机制来提高摘要质量。实验结果表明，该模型明显优于抽象和提取式模型，经常生成未包含在原始文本中的新单词。

基于强化学习的无监督句子自动摘要系统生成多种长度的摘要

Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization

Opinion summarization is the task of automatically generating summaries for a
set of reviews about a specific target (e.g., a movie or a product). Since the
number of reviews for each target can be prohibitively large, neural
network-based methods follow a two-stage approach where an extractive step
first pre-selects a subset of salient opinions and an abstractive step creates
the summary while conditioning on the extracted subset. However, the extractive
model leads to loss of information which may be useful depending on user needs.
In this paper we propose a summarization framework that eliminates the need to
rely only on pre-selected content and waste possibly useful information,
especially when customizing summaries. The framework enables the use of all
input reviews by first condensing them into multiple dense vectors which serve
as input to an abstractive model. We showcase an effective instantiation of our
framework which produces more informative summaries and also allows to take
user preferences into account using our zero-shot customization technique.
Experimental results demonstrate that our model improves the state of the art
on the Rotten Tomatoes dataset and generates customized summaries effectively.

本文提出了一种摘要框架，通过将目标的所有评论压缩为多个密集向量以代替传统的前置筛选步骤，从而最大化地保留所有信息，进而生成更具信息量的摘要，并且采用零样本技术，能够有效地生成根据用户需求定制的摘要，实验结果表明了该模型在 Rotten Tomatoes 数据集上的优越性能。

信息丰富且可控的观点摘要

Informative and Controllable Opinion Summarization

We introduce the first large-scale corpus for long-form question answering, a
task requiring elaborate and in-depth answers to open-ended questions. The
dataset comprises 270K threads from the Reddit forum ``Explain Like I'm Five''
(ELI5) where an online community provides answers to questions which are
comprehensible by five year olds. Compared to existing datasets, ELI5 comprises
diverse questions requiring multi-sentence answers. We provide a large set of
web documents to help answer the question. Automatic and human evaluations show
that an abstractive model trained with a multi-task objective outperforms
conventional Seq2Seq, language modeling, as well as a strong extractive
baseline. However, our best model is still far from human performance since
raters prefer gold responses in over 86% of cases, leaving ample opportunity
for future improvement.

这篇论文介绍了第一个大规模的长篇问答语料库，其中包括了 270,000 个 Reddit 论坛上的线索，使用多任务目标的诠释性模型表现最佳，但仍存在改进的余地。