Summarizing text-rich documents has been long studied in the literature, but
most of the existing efforts have been made to summarize a static and
predefined multi-document set. With the rapid development of online platforms
for generating and distributing text-rich documents, there arises an urgent
need for continuously summarizing dynamically evolving multi-document sets
where the composition of documents and sets is changing over time. This is
especially challenging as the summarization should be not only effective in
incorporating relevant, novel, and distinctive information from each concurrent
multi-document set, but also efficient in serving online applications. In this
work, we propose a new summarization problem, Evolving Multi-Document sets
stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum
with the idea of prototype-driven continuous summarization. PDSum builds a
lightweight prototype of each multi-document set and exploits it to adapt to
new documents while preserving accumulated knowledge from previous documents.
To update new summaries, the most representative sentences for each
multi-document set are extracted by measuring their similarities to the
prototypes. A thorough evaluation with real multi-document sets streams
demonstrates that PDSum outperforms state-of-the-art unsupervised
multi-document summarization algorithms in EMDS in terms of relevance, novelty,
and distinctiveness and is also robust to various evaluation settings.

这篇论文提出了一种新的问题 Evolution Multi-Document sets stream Summarization (EMDS) 以及一种新的无监督算法 PDSum 来解决动态演变的多文档集的连续摘要，该算法采用基于原型的轻量级方法来更新新摘要。

PDSum：基于原型的流式多文档集持续摘要

PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream

The difficulty of generating coherent long texts lies in the fact that
existing models overwhelmingly focus on predicting local words, and cannot make
high level plans on what to generate or capture the high-level discourse
dependencies between chunks of texts. Inspired by human writing processes,
where a list of bullet points or a catalog is first outlined, and then each
bullet point is expanded to form the whole article, we propose {\it SOE}, a
pipelined system that involves of summarizing, outlining and elaborating for
long text generation: the model first outlines the summaries for different
segments of long texts, and then elaborates on each bullet point to generate
the corresponding segment. To avoid the labor-intensive process of summary
soliciting, we propose the {\it reconstruction} strategy, which extracts
segment summaries in an unsupervised manner by selecting its most informative
part to reconstruct the segment. The proposed generation system comes with the
following merits: (1) the summary provides high-level guidance for text
generation and avoids the local minimum of individual word predictions; (2) the
high-level discourse dependencies are captured in the conditional dependencies
between summaries and are preserved during the summary expansion process and
(3) additionally, we are able to consider significantly more contexts by
representing contexts as concise summaries. Extensive experiments demonstrate
that SOE produces long texts with significantly better quality, along with
faster convergence speed.

提出了一种新的生成长文本的方法 SOE，它采用了概述、大纲和扩展的方法来为每个段落生成高效的文本，并通过重构策略无监督地提取每个段落的摘要以避免繁琐的总结过程。

通过从摘要中进行分层监督进行长文本生成的总结，概述和阐述

Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical  Supervision from Extractive Summaries

We describe a new method for summarizing similarities and differences in a
pair of related documents using a graph representation for text. Concepts
denoted by words, phrases, and proper names in the document are represented
positionally as nodes in the graph along with edges corresponding to semantic
relations between items. Given a perspective in terms of which the pair of
documents is to be summarized, the algorithm first uses a spreading activation
technique to discover, in each document, nodes semantically related to the
topic. The activated graphs of each document are then matched to yield a graph
corresponding to similarities and differences between the pair, which is
rendered in natural language. An evaluation of these techniques has been
carried out.

本论文提出了一种基于图表达的文本相似性和差异性总结方法，使用 “spreading activation” 技术在两个相关文档中发现与主题语义相关的节点，并将两个文档的激活图匹配以获得相似和不同之处的图。