This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discriminator module. The coherent discriminator is trained online on the sentence vectors of the augmented textual input, thus improving its general ability of judging whether the input sentences are coherent. Meanwhile, we maximize the coherent scores from the coherent discriminator by updating the parameters of the summarizer. To make the extractive sentences trainable in a differentiable manner, we introduce two strategies, including pre-trained converting model (model-based) and converting matrix (MAT-based) that merge sentence representations. Experiments show that our proposed method significantly improves the proportion of consecutive sentences in the extracted summaries based on their positions in the original article (i.e., automatic sentence-level coherence metric), while the goodness in terms of other automatic metrics (i.e., Rouge scores and BertScores) are preserved. Human evaluation also evidences the improvement of coherence and consistency of the extracted summaries given by our method.

翻译：本研究提出了一种面向抽取式摘要的多任务学习架构，旨在提升摘要连贯性。该架构包含抽取式摘要器与连贯性判别器模块。连贯性判别器通过在增强后的文本输入句向量上进行在线训练，从而提升其对输入句子连贯性的通用判断能力。同时，通过更新摘要器参数，最大化连贯性判别器输出的连贯性得分。为使抽取式句子能够以可微方式训练，我们引入了两种策略：预训练转换模型（基于模型）与转换矩阵（基于MAT），两者均用于合并句子表示。实验表明，所提方法在保持其他自动评估指标（如Rouge分数与BertScore）优异表现的同时，显著提升了抽取摘要中基于原文位置关系的连续句子比例（即句子级自动连贯性指标）。人工评估结果也证实了本方法在提升摘要连贯性与一致性方面的有效性。