Modern deep models for summarization attains impressive benchmark
performance, but they are prone to generating miscalibrated predictive
uncertainty. This means that they assign high confidence to low-quality
predictions, leading to compromised reliability and trustworthiness in
real-world applications. Probabilistic deep learning methods are common
solutions to the miscalibration problem. However, their relative effectiveness
in complex autoregressive summarization tasks are not well-understood. In this
work, we thoroughly investigate different state-of-the-art probabilistic
methods' effectiveness in improving the uncertainty quality of the neural
summarization models, across three large-scale benchmarks with varying
difficulty. We show that the probabilistic methods consistently improve the
model's generation and uncertainty quality, leading to improved selective
generation performance (i.e., abstaining from low-quality summaries) in
practice. We also reveal notable failure patterns of probabilistic methods
widely-adopted in NLP community (e.g., Deep Ensemble and Monte Carlo Dropout),
cautioning the importance of choosing appropriate method for the data setting.

我们研究了深度学习方法在文本摘要中存在的局限性，提出使用概率深度学习方法来提高模型生成与不确定性质量， 并在实践中采用适当的方法来避免摘要生成的低质量问题。

关于概率神经摘要中的不确定性校准和选择性生成：一项基准研究

On Uncertainty Calibration and Selective Generation in Probabilistic  Neural Summarization: A Benchmark Study

Summarizing texts is not a straightforward task. Before even considering text
summarization, one should determine what kind of summary is expected. How much
should the information be compressed? Is it relevant to reformulate or should
the summary stick to the original phrasing? State-of-the-art on automatic text
summarization mostly revolves around news articles. We suggest that considering
a wider variety of tasks would lead to an improvement in the field, in terms of
generalization and robustness. We explore meeting summarization: generating
reports from automatic transcriptions. Our work consists in segmenting and
aligning transcriptions with respect to reports, to get a suitable dataset for
neural summarization. Using a bootstrapping approach, we provide pre-alignments
that are corrected by human annotators, making a validation set against which
we evaluate automatic models. This consistently reduces annotators' efforts by
providing iteratively better pre-alignment and maximizes the corpus size by
using annotations from our automatic alignment models. Evaluation is conducted
on \publicmeetings, a novel corpus of aligned public meetings. We report
automatic alignment and summarization performances on this corpus and show that
automatic alignment is relevant for data annotation since it leads to large
improvement of almost +4 on all ROUGE scores on the summarization task.

本文提出了使用会议摘要生成自动转录报告的方法。使用自动预对齐方法使数据批量化对齐，最大化语料库的规模，以及使用人工标注者进行更准确地校对。作者还评估了自动对齐和自动摘要的表现，并证明自动对齐可大幅度提高所有 ROUGE 得分。

自动对齐方法用于摘要语料库构建

Align then Summarize: Automatic Alignment Methods for Summarization  Corpus Creation

We present a novel divide-and-conquer method for the neural summarization of
long documents. Our method exploits the discourse structure of the document and
uses sentence similarity to split the problem into an ensemble of smaller
summarization problems. In particular, we break a long document and its summary
into multiple source-target pairs, which are used for training a model that
learns to summarize each part of the document separately. These partial
summaries are then combined in order to produce a final complete summary. With
this approach we can decompose the problem of long document summarization into
smaller and simpler problems, reducing computational complexity and creating
more training examples, which at the same time contain less noise in the target
summaries compared to the standard approach. We demonstrate that this approach
paired with different summarization models, including sequence-to-sequence RNNs
and Transformers, can lead to improved summarization performance. Our best
models achieve results that are on par with the state-of-the-art in two two
publicly available datasets of academic articles.

该研究提出了一种新颖的分治法神经摘要长文档的方法，结合话语结构和句子相似性将长文档分解为多个摘要子问题。通过该方法可以有效地降低计算复杂度，提高摘要效果。

长文档摘要的分而治之方法

A Divide-and-Conquer Approach to the Summarization of Long Documents

An abstract must not change the meaning of the original text. A single most
effective way to achieve that is to increase the amount of copying while still
allowing for text abstraction. Human editors can usually exercise control over
copying, resulting in summaries that are more extractive than abstractive, or
vice versa. However, it remains poorly understood whether modern neural
abstractive summarizers can provide the same flexibility, i.e., learning from
single reference summaries to generate multiple summary hypotheses with varying
degrees of copying. In this paper, we present a neural summarization model
that, by learning from single human abstracts, can produce a broad spectrum of
summaries ranging from purely extractive to highly generative ones. We frame
the task of summarization as language modeling and exploit alternative
mechanisms to generate summary hypotheses. Our method allows for control over
copying during both training and decoding stages of a neural summarization
model. Through extensive experiments we illustrate the significance of our
proposed method on controlling the amount of verbatim copying and achieve
competitive results over strong baselines. Our analysis further reveals
interesting and unobvious facts.

通过学习自单一人类摘要信息，我们提出了一种神经网络摘要模型，该模型可以生成从纯粹提取式到高度生成式的多种摘要假设，并在控制文本复制的同时实现训练和解码阶段内的控制，从而实现了竞争性的结果。