Creating an abridged version of a text involves shortening it while maintaining its linguistic qualities. In this paper, we examine this task from an NLP perspective for the first time. We present a new resource, AbLit, which is derived from abridged versions of English literature books. The dataset captures passage-level alignments between the original and abridged texts. We characterize the linguistic relations of these alignments, and create automated models to predict these relations as well as to generate abridgements for new texts. Our findings establish abridgement as a challenging task, motivating future resources and research. The dataset is available at github.com/roemmele/AbLit.

本文首次从NLP角度研究了创建文本的节选版本这一问题。我们创造了新的资源AbLit，该数据集基于英文文学书籍的节选版本，捕捉了原始文本和节选版本之间的段落级对齐关系。我们描述了这些对齐的语言关系，并创建了自动化模型来预测这些关系以及为新文本生成缩写。我们的研究结果确定了缩写版本创建作为一项具有挑战性的任务，并为未来的资源和研究提供了动力。该数据集可通过此 http URL获得。

AbLit：用于分析和生成英语文学节选版本的资源