  Due to the subjectivity of the summarization, it is a good practice to have
more than one gold summary for each training document. However, many modern
large-scale abstractive summarization datasets have only one-to-one samples
written by different human with different styles. The impact of this phenomenon
is understudied. We formulate the differences among possible multiple
expressions summarizing the same content as subjective bias and examine the
role of this bias in the context of abstractive summarization. In this paper a
lightweight and effective method to extract the feature embeddings of
subjective styles is proposed. Results of summarization models trained on
style-clustered datasets show that there are certain types of styles that lead
to better convergence, abstraction and generalization. The reproducible code
and generated summaries are available online.
