We provide a quantitative and qualitative analysis of self-repetition in the
output of neural summarizers. We measure self-repetition as the number of
n-grams of length four or longer that appear in multiple outputs of the same
system. We analyze the behavior of three popular architectures (BART, T5, and
Pegasus), fine-tuned on five datasets. In a regression analysis, we find that
the three architectures have different propensities for repeating content
across output summaries for inputs, with BART being particularly prone to
self-repetition. Fine-tuning on more abstractive data, and on data featuring
formulaic language, is associated with a higher rate of self-repetition. In
qualitative analysis we find systems produce artefacts such as ads and
disclaimers unrelated to the content being summarized, as well as formulaic
phrases common in the fine-tuning domain. Our approach to corpus-level analysis
of self-repetition may help practitioners clean up training data for
summarizers and ultimately support methods for minimizing the amount of
self-repetition.

对神经文摘的自我重复进行定量和定性分析。分析了三种神经网络结构（BART，T5 和 Pegasus），发现 BART 特别容易出现自我重复，并且 fine-tuning 在更具抽象性的数据和在特定领域的数据上都会导致更高的自我重复率。

抽象神经摘要器中的自我重复性

Self-Repetition in Abstractive Neural Summarizers

Proposal of large-scale datasets has facilitated research on deep neural
models for news summarization. Deep learning can also be potentially useful for
spoken dialogue summarization, which can benefit a range of real-life scenarios
including customer service management and medication tracking. To this end, we
propose DialogSum, a large-scale labeled dialogue summarization dataset. We
conduct empirical analysis on DialogSum using state-of-the-art neural
summarizers. Experimental results show unique challenges in dialogue
summarization, such as spoken terms, special discourse structures, coreferences
and ellipsis, pragmatics and social common sense, which require specific
representation learning technologies to better deal with.

提出了 DialogSum，一种大规模标注的对话摘要数据集，通过对该数据集使用最先进的神经摘要器进行经验分析，结果表明对话摘要中存在诸如口语术语、特殊话语结构、代词和省略、语用学和社会常识等独特挑战，需要特定的表示学习技术来更好地处理。