Construction of human-curated annotated datasets for abstractive text
summarization (ATS) is very time-consuming and expensive because creating each
instance requires a human annotator to read a long document and compose a
shorter summary that would preserve the key information relayed by the original
document. Active Learning (AL) is a technique developed to reduce the amount of
annotation required to achieve a certain level of machine learning model
performance. In information extraction and text classification, AL can reduce
the amount of labor up to multiple times. Despite its potential for aiding
expensive annotation, as far as we know, there were no effective AL query
strategies for ATS. This stems from the fact that many AL strategies rely on
uncertainty estimation, while as we show in our work, uncertain instances are
usually noisy, and selecting them can degrade the model performance compared to
passive annotation. We address this problem by proposing the first effective
query strategy for AL in ATS based on diversity principles. We show that given
a certain annotation budget, using our strategy in AL annotation helps to
improve the model performance in terms of ROUGE and consistency scores.
Additionally, we analyze the effect of self-learning and show that it can
further increase the performance of the model.

本文提出了第一个基于多样性原则的主动学习查询策略来缓解抽象文本摘要中昂贵的注释成本，并分析了自学习的效果。该策略可以帮助提高 ROUGE 和一致性分数。