Multi-document summarization (MDS) is a challenging task, often decomposed to
subtasks of salience and redundancy detection, followed by text generation. In
this context, alignment of corresponding sentences between a reference summary
and its source documents has been leveraged to generate training data for some
of the component tasks. Yet, this enabling alignment step has usually been
applied heuristically on the sentence level on a limited number of subtasks. In
this paper, we propose extending the summary-source alignment framework by (1)
applying it at the more fine-grained proposition span level, (2) annotating
alignment manually in a multi-document setup, and (3) revealing the great
potential of summary-source alignments to yield several datasets for at least
six different tasks. Specifically, for each of the tasks, we release a manually
annotated test set that was derived automatically from the alignment
annotation. We also release development and train sets in the same way, but
from automatically derived alignments. Using the datasets, each task is
demonstrated with baseline models and corresponding evaluation metrics to spur
future research on this broad challenge.

扩展摘要 - 源数据对齐框架，应用于提取多个任务的数据集，包括文档摘要、内容突出点检测、冗余检测等。

摘要 - 来源对齐的力量

The Power of Summary-Source Alignments

Measuring event salience is essential in the understanding of stories. This
paper takes a recent unsupervised method for salience detection derived from
Barthes Cardinal Functions and theories of surprise and applies it to longer
narrative forms. We improve the standard transformer language model by
incorporating an external knowledgebase (derived from Retrieval Augmented
Generation) and adding a memory mechanism to enhance performance on longer
works. We use a novel approach to derive salience annotation using
chapter-aligned summaries from the Shmoop corpus for classic literary works.
Our evaluation against this data demonstrates that our salience detection model
improves performance over and above a non-knowledgebase and memory augmented
language model, both of which are crucial to this improvement.

本研究利用 Barthes Cardinal Functions 和 surprise 和理论为基础的无监督方法，将其应用于长篇叙事形式中，改进了标准 transformer 语言模型，通过引入 Retrieval Augmented Generation 的外部知识库和添加记忆机制来提高该语言模型在长篇作品中的性能，使用来自 Shmoop 语料库的章节摘要来推导显著性标注的新方法，针对这些数据进行的评估证明，与非知识库和记忆增强语言模型相比，我们的显著性检测模型提高了性能，这两者对于此改善至关重要。

用记忆和知识增强的语言模型推断长篇故事的关键性

Memory and Knowledge Augmented Language Models for Inferring Salience in  Long-Form Stories

Abstractive summarization is the task of compressing a long document into a
coherent short document while retaining salient information. Modern abstractive
summarization methods are based on deep neural networks which often require
large training datasets. Since collecting summarization datasets is an
expensive and time-consuming task, practical industrial settings are usually
low-resource. In this paper, we study a challenging low-resource setting of
summarizing long legal briefs with an average source document length of 4268
words and only 120 available (document, summary) pairs. To account for data
scarcity, we used a modern pretrained abstractive summarizer BART (Lewis et
al., 2020), which only achieves 17.9 ROUGE-L as it struggles with long
documents. We thus attempt to compress these long documents by identifying
salient sentences in the source which best ground the summary, using a novel
algorithm based on GPT-2 (Radford et al., 2019) language model perplexity
scores, that operates within the low resource regime. On feeding the compressed
documents to BART, we observe a 6.0 ROUGE-L improvement. Our method also beats
several competitive salience detection baselines. Furthermore, the identified
salient sentences tend to agree with an independent human labeling by domain
experts.

本文探讨如何在低资源情况下，使用深度神经网络等技术进行长篇法律文件的自动摘要，本文提出了一种基于 GPT-2 的算法，基于语言模型的困惑度，识别出最具有表现力的句子，在提取摘要时提供有效支持，并且该方法胜过了全球其他对手的显著度检测基线。