  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.
