Multi-document summarization is the process of automatically generating a
concise summary of multiple documents related to the same topic. This summary
can help users quickly understand the key information from a large collection
of documents. Multi-document summarization systems are more complex than
single-document summarization systems due to the need to identify and combine
information from multiple sources. In this paper, we have developed a machine
learning model that generates a concise summary of a topic from multiple news
documents. The model is designed to be unbiased by sampling its input equally
from all the different aspects of the topic, even if the majority of the news
sources lean one way.

多文档摘要是自动生成与同一主题相关的多个文档的简洁摘要的过程。本文提出了一个机器学习模型，从多个新闻文档中生成主题的简洁摘要，并设计成能够公正地从各个方面均等采样输入信息，即使大多数新闻来源倾向于一方。

利用多源新闻摘要塑造政治话语

Shaping Political Discourse using multi-source News Summarization

While there has been substantial progress in text comprehension through
simple factoid question answering, more holistic comprehension of a discourse
still presents a major challenge (Dunietz et al., 2020). Someone critically
reflecting on a text as they read it will pose curiosity-driven, often
open-ended questions, which reflect deep understanding of the content and
require complex reasoning to answer (Ko et al., 2020; Westera et al., 2020). A
key challenge in building and evaluating models for this type of discourse
comprehension is the lack of annotated data, especially since collecting
answers to such questions requires high cognitive load for annotators. This
paper presents a novel paradigm that enables scalable data collection targeting
the comprehension of news documents, viewing these questions through the lens
of discourse. The resulting corpus, DCQA (Discourse Comprehension by Question
Answering), captures both discourse and semantic links between sentences in the
form of free-form, open-ended questions. On an evaluation set that we annotated
on questions from Ko et al. (2020), we show that DCQA provides valuable
supervision for answering open-ended questions. We additionally design
pre-training methods utilizing existing question-answering resources, and use
synthetic data to accommodate unanswerable questions.

本文提出了一种新的方法来收集关于新闻文件的提问，带有上下文关联且没有标注答案的开放性问题数据集 (DCQA)，并结合现有的问答资源进行了预训练，以有效帮助回答这类开放性问题。

话语理解：一种问答框架用于表示句子之间的联系

Discourse Comprehension: A Question Answering Framework to Represent  Sentence Connections

We investigate the problem of reader-aware multi-document summarization
(RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we
extend a variational auto-encodes (VAEs) based MDS framework by jointly
considering news documents and reader comments. To conduct evaluation for
summarization performance, we prepare a new dataset. We describe the methods
for data collection, aspect annotation, and summary writing as well as
scrutinizing by experts. Experimental results show that reader comments can
improve the summarization performance, which also demonstrates the usefulness
of the proposed dataset. The annotated dataset for RA-MDS is available online.

本文介绍了一个新的数据集来解决阅读者感知的多文档摘要问题；通过扩展基于变分自动编解码器的 MDS 框架，共同考虑新闻文档和读者评论。实验结果表明，读者评论能够提高摘要的表现，并证明了所提出的数据集的实用性。