Multi-document summarization (MDS) refers to the task of summarizing the text
in multiple documents into a concise summary. The generated summary can save
the time of reading many documents by providing the important content in the
form of a few sentences. Abstractive MDS aims to generate a coherent and fluent
summary for multiple documents using natural language generation techniques. In
this paper, we consider the unsupervised abstractive MDS setting where there
are only documents with no groundtruh summaries provided, and we propose
Absformer, a new Transformer-based method for unsupervised abstractive summary
generation. Our method consists of a first step where we pretrain a
Transformer-based encoder using the masked language modeling (MLM) objective as
the pretraining task in order to cluster the documents into semantically
similar groups; and a second step where we train a Transformer-based decoder to
generate abstractive summaries for the clusters of documents. To our knowledge,
we are the first to successfully incorporate a Transformer-based model to solve
the unsupervised abstractive MDS task. We evaluate our approach using three
real-world datasets from different domains, and we demonstrate both substantial
improvements in terms of evaluation metrics over state-of-the-art
abstractive-based methods, and generalization to datasets from different
domains.

本文提出了一种新的基于 Transformer 的方法，称为 Absformer，旨在针对没有提供摘要总结的文档进行无监督的抽象式多文档摘要生成，并在现实世界的三个不同领域的数据集上进行了评估，结果表明该方法在评价指标方面取得了实质性的提高，并且具有从不同领域的数据集中泛化的优势。

Absformer: 基于 Transformer 的无监督多文档抽象摘要模型

Absformer: Transformer-based Model for Unsupervised Multi-Document  Abstractive Summarization

Dialogue summarization aims to condense the original dialogue into a shorter
version covering salient information, which is a crucial way to reduce dialogue
data overload. Recently, the promising achievements in both dialogue systems
and natural language generation techniques drastically lead this task to a new
landscape, which results in significant research attentions. However, there
still remains a lack of a comprehensive survey for this task. To this end, we
take the first step and present a thorough review of this research field
carefully and widely. In detail, we systematically organize the current works
according to the characteristics of each domain, covering meeting, chat, email
thread, customer service and medical dialogue. Additionally, we provide an
overview of publicly available research datasets as well as organize two
leaderboards under unified metrics. Furthermore, we discuss some future
directions, including faithfulness, multi-modal, multi-domain and multi-lingual
dialogue summarization, and give our thoughts respectively. We hope that this
first survey of dialogue summarization can provide the community with a quick
access and a general picture to this task and motivate future researches.

对话摘要化是将原始对话压缩成涵盖重要信息的短版，用于减轻对话数据负载，本研究从多个领域（包括会议，聊天，电子邮件，客户服务和医疗对话）全面系统地调查了对话摘要化的现状和研究进展，包括可用于研究数据集的总览和两个排行榜，并提出了一些未来的研究方向，如忠实度、多模态、多领域和多语言对话摘要化。