In this paper, we present RTSUM, an unsupervised summarization framework that
utilizes relation triples as the basic unit for summarization. Given an input
document, RTSUM first selects salient relation triples via multi-level salience
scoring and then generates a concise summary from the selected relation triples
by using a text-to-text language model. On the basis of RTSUM, we also develop
a web demo for an interpretable summarizing tool, providing fine-grained
interpretations with the output summary. With support for customization
options, our tool visualizes the salience for textual units at three distinct
levels: sentences, relation triples, and phrases. The codes,are publicly
available.

RTSUM 是一种无监督的摘要框架，利用关系三元组作为摘要的基本单位。在 RTSUM 的基础上，我们还开发了一个可解释的摘要工具的网络演示，提供了与输出摘要相关的精细解释。

RTSUM: 基于关系三元组的多层显著可视化的可解释摘要

RTSUM: Relation Triple-based Interpretable Summarization with  Multi-level Salience Visualization

Unsupervised summarization is a powerful technique that enables training
summarizing models without requiring labeled datasets. This survey covers
different recent techniques and models used for unsupervised summarization. We
cover extractive, abstractive, and hybrid models and strategies used to achieve
unsupervised summarization. While the main focus of this survey is on recent
research, we also cover some of the important previous research. We
additionally introduce a taxonomy, classifying different research based on
their approach to unsupervised training. Finally, we discuss the current
approaches and mention some datasets and evaluation methods.

本调查涵盖了用于无监督摘要的不同技术和模型，包括抽取式摘要、生成式摘要和混合模型，提出了一种分类方法，并讨论了当前的方法、一些数据集和评估方法。

无监督摘要生成的最近趋势

Recent Trends in Unsupervised Summarization

Unsupervised summarization methods have achieved remarkable results by
incorporating representations from pre-trained language models. However,
existing methods fail to consider efficiency and effectiveness at the same time
when the input document is extremely long. To tackle this problem, in this
paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR)
framework for unsupervised long document summarization, which is based on the
semantic block. The semantic block refers to continuous sentences in the
document that describe the same facet. Specifically, we address this problem by
converting the one-step ranking method into the hierarchical multi-granularity
two-stage ranking. In the coarse-level stage, we propose a new segment
algorithm to split the document into facet-aware semantic blocks and then
filter insignificant blocks. In the fine-level stage, we select salient
sentences in each block and then extract the final summary from selected
sentences. We evaluate our framework on four long document summarization
datasets: Gov-Report, BillSum, arXiv, and PubMed. Our C2F-FAR can achieve new
state-of-the-art unsupervised summarization results on Gov-Report and BillSum.
In addition, our method speeds up 4-28 times more than previous
methods.\footnote{https://github.com/xnliang98/c2f-far}

提出了一种基于语义块和效率的分层多粒度两阶段排序的无监督长文档摘要方法 C2F-FAR，可以在 Gov-Report 和 BillSum 数据集上获得新的最先进的无监督摘要结果，并且比之前的方法速度提高了 4-28 倍。