Text Summarization is recognised as one of the NLP downstream tasks and it
has been extensively investigated in recent years. It can assist people with
perceiving the information rapidly from the Internet, including news articles,
social posts, videos, etc. Most existing research works attempt to develop
summarization models to produce a better output. However, advent limitations of
most existing models emerge, including unfaithfulness and factual errors. In
this paper, we propose a novel model, named as Knowledge-aware Abstractive Text
Summarization, which leverages the advantages offered by Knowledge Graph to
enhance the standard Seq2Seq model. On top of that, the Knowledge Graph
triplets are extracted from the source text and utilised to provide keywords
with relational information, producing coherent and factually errorless
summaries. We conduct extensive experiments by using real-world data sets. The
results reveal that the proposed framework can effectively utilise the
information from Knowledge Graph and significantly reduce the factual errors in
the summary.

本文提出了一种基于知识图谱的抽象文本摘要模型（Knowledge-aware Abstractive Text Summarization），其利用源文本中提取的知识图谱三元组来提供具有关系信息的关键词，生成连贯且无误差的摘要，试验表明，该框架可以有效利用知识图谱的信息，显著减少摘要中的事实错误。

KATSum: 知识感知抽象文本摘要

KATSum: Knowledge-aware Abstractive Text Summarization

Abstractive dialogue summarization is to generate a concise and fluent
summary covering the salient information in a dialogue among two or more
interlocutors. It has attracted great attention in recent years based on the
massive emergence of social communication platforms and an urgent requirement
for efficient dialogue information understanding and digestion. Different from
news or articles in traditional document summarization, dialogues bring unique
characteristics and additional challenges, including different language styles
and formats, scattered information, flexible discourse structures and unclear
topic boundaries. This survey provides a comprehensive investigation on
existing work for abstractive dialogue summarization from scenarios, approaches
to evaluations. It categorizes the task into two broad categories according to
the type of input dialogues, i.e., open-domain and task-oriented, and presents
a taxonomy of existing techniques in three directions, namely, injecting
dialogue features, designing auxiliary training tasks and using additional
data.A list of datasets under different scenarios and widely-accepted
evaluation metrics are summarized for completeness. After that, the trends of
scenarios and techniques are summarized, together with deep insights on
correlations between extensively exploited features and different scenarios.
Based on these analyses, we recommend future directions including more
controlled and complicated scenarios, technical innovations and comparisons,
publicly available datasets in special domains, etc.

调查了目前存在的抽象对话总结的工作，包括输入对话的类型、途径、评估等，并列出了在不同情景下的数据集和常用的评估度量，分析了趋势和未来发展方向。

抽象化对话摘要分类：情景、方法与未来方向

Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions

Existing summarization systems mostly generate summaries purely relying on
the content of the source document. However, even for humans, we usually need
some references or exemplars to help us fully understand the source document
and write summaries in a particular format. But how to find the high-quality
exemplars and incorporate them into summarization systems is still challenging
and worth exploring. In this paper, we propose RetrievalSum, a novel retrieval
enhanced abstractive summarization framework consisting of a dense Retriever
and a Summarizer. At first, several closely related exemplars are retrieved as
supplementary input to help the generation model understand the text more
comprehensively. Furthermore, retrieved exemplars can also play a role in
guiding the model to capture the writing style of a specific corpus. We
validate our method on a wide range of summarization datasets across multiple
domains and two backbone models: BERT and BART. Results show that our framework
obtains significant improvement by 1.38~4.66 in ROUGE-1 score when compared
with the powerful pre-trained models, and achieve new state-of-the-art on
BillSum. Human evaluation demonstrates that our retrieval enhanced model can
better capture the domain-specific writing style.

该论文提出了 RetrievalSum，一种新颖的检索增强抽象概括框架，包括一个稠密的 Retriever 和一个 Summarizer，可以通过检索相关的用作补充输入的实例来帮助生成模型更全面地理解文本，并在 BillSum 上取得了最新的业界最佳成绩。

RetrievalSum：基于检索增强的摘要生成框架

RetrievalSum: A Retrieval Enhanced Framework for Abstractive  Summarization

We present NEWSROOM, a summarization dataset of 1.3 million articles and
summaries written by authors and editors in newsrooms of 38 major news
publications. Extracted from search and social media metadata between 1998 and
2017, these high-quality summaries demonstrate high diversity of summarization
styles. In particular, the summaries combine abstractive and extractive
strategies, borrowing words and phrases from articles at varying rates. We
analyze the extraction strategies used in NEWSROOM summaries against other
datasets to quantify the diversity and difficulty of our new data, and train
existing methods on the data to evaluate its utility and challenges.

利用搜索和社交媒体元数据，我们构建了 1.3 百万篇文章及其摘要的 NEWSROOM 数据集，包含不同风格的抽象和抽取策略，为评估该数据集的效用和挑战，我们对该数据集进行了分析和训练现有方法。