Sequence-level knowledge distillation reduces the size of Seq2Seq models for
more efficient abstractive summarization. However, it often leads to a loss of
abstractiveness in summarization. In this paper, we propose a novel approach
named DisCal to enhance the level of abstractiveness (measured by n-gram
overlap) without sacrificing the informativeness (measured by ROUGE) of
generated summaries. DisCal exposes diverse pseudo summaries with two
supervision to the student model. Firstly, the best pseudo summary is
identified in terms of abstractiveness and informativeness and used for
sequence-level distillation. Secondly, their ranks are used to ensure the
student model to assign higher prediction scores to summaries with higher
ranks. Our experiments show that DisCal outperforms prior methods in
abstractive summarization distillation, producing highly abstractive and
informative summaries.

提出了一种名为 DisCal 的新方法，通过引入两种监督方式，提高生成摘要的抽象性（通过 n-gram 重叠度量），同时不牺牲信息量（通过 ROUGE 度量），并在摘要提取中优于之前的方法。

通过校准蒸馏增强摘要模型的抽象性

Enhancing Abstractiveness of Summarization Models through Calibrated  Distillation

The abstractive methods lack of creative ability is particularly a problem in
automatic text summarization. The summaries generated by models are mostly
extracted from the source articles. One of the main causes for this problem is
the lack of dataset with abstractiveness, especially for Chinese. In order to
solve this problem, we paraphrase the reference summaries in CLTS, the Chinese
Long Text Summarization dataset, correct errors of factual inconsistencies, and
propose the first Chinese Long Text Summarization dataset with a high level of
abstractiveness, CLTS+, which contains more than 180K article-summary pairs and
is available online. Additionally, we introduce an intrinsic metric based on
co-occurrence words to evaluate the dataset we constructed. We analyze the
extraction strategies used in CLTS+ summaries against other datasets to
quantify the abstractiveness and difficulty of our new data and train several
baselines on CLTS+ to verify the utility of it for improving the creative
ability of models.

为提高模型的创造能力，该研究构建了含超过 180K 篇文章 - 摘要对的具有高度抽象性的中文长文本摘要数据集（CLTS+），并提出了一种基于共现词的评估该数据集的内在度量方法。

CLTS+: 一个新的中文长文本摘要数据集，具有生成式摘要

CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries

We analyze the tradeoff between factuality and abstractiveness of summaries.
We introduce abstractiveness constraints to control the degree of
abstractiveness at decoding time, and we apply this technique to characterize
the abstractiveness-factuality tradeoff across multiple widely-studied
datasets, using extensive human evaluations. We train a neural summarization
model on each dataset and visualize the rates of change in factuality as we
gradually increase abstractiveness using our abstractiveness constraints. We
observe that, while factuality generally drops with increased abstractiveness,
different datasets lead to different rates of factuality decay. We propose new
measures to quantify the tradeoff between factuality and abstractiveness, incl.
muQAGS, which balances factuality with abstractiveness. We also quantify this
tradeoff in previous works, aiming to establish baselines for the
abstractiveness-factuality tradeoff that future publications can compare
against.

本文研究自动摘要的抽象程度与准确度之间的权衡，提出了控制抽象程度的技术，并使用全面的人工评估方法，在多个广泛研究的数据集上表征抽象 - 准确度权衡关系，提出了新的量化措施，并以此与以前的作品对比基线。