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：本研究提出一种面向连贯性增强的抽取式摘要多任务学习架构。该架构包含抽取式摘要器和连贯性判别模块。连贯性判别器通过对增强文本输入的句子向量进行在线训练，从而提升其判断输入句子连贯性的泛化能力。同时，本文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。为实现抽取句子的可微训练，我们引入两种策略：基于预训练转换模型（模型驱动法）和基于转换矩阵（MAT驱动法）的句子表示融合策略。实验表明，本方法显著提升了提取摘要中基于原文位置的连续句比例（即自动句子级连贯性指标），同时保持了其他自动评估指标（Rouge分数和BertScores）的优异性能。人工评估也证实了本方法生成的摘要具有更强的连贯性和一致性。