AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3717–3733 May 2-6, 2023 ©2023 Association for Computational Linguistics ABLIT: A Resource for Analyzing and Generating Abridged Versions of English Literature Melissa Roemmele Kyle Shaffer Katrina Olsen Yiyi Wang Steve DeNeefe Language Weaver (RWS Group) {mroemmele,kshaffer,kolsen,yiyiwang,sdeneefe}@rws.com Abstract 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 de- rived from abridged versions of English litera- ture 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 estab- lish abridgement as a challenging task, motivat- ing future resources and research. The dataset is available at github.com/roemmele/AbLit. 1 Introduction An abridgement is a shortened form of a text that maintains the linguistic qualities of that text1. It is intended to make the original text faster and easier to read. In this paper, we propose abridgement as an NLP problem and describe its connection to ex- isting inference and generation tasks. We present a novel dataset for this task, focused on abridged versions of English literature books, which we re- fer to as the ABLIT dataset. We demonstrate the characteristics of ABLIT in terms of the relations between original and abridged texts as well as the challenges of automatically modeling these rela- tions. The dataset and all associated code, includ- ing a Python package for easily interfacing with the data, are available at: github.com/roemmele/AbLit. 2 The task of abridgement 2.1 Definition We define abridgement as the task of making a text easier to understand while preserving its linguistic 1The term “linguistic qualities” is broad, which reflects other definitions of abridgement. For instance, the Wikipedia entry for “abridgement” specifies that it “maintains the unity of the source”, but these dimensions of unity are tacitly defined. qualities. As such, abridgement intersects with tasks that fuse natural language inference (NLI) and natural language generation (NLG), in particular summarization and simplification. Summarization condenses the main content of a text into a shorter version in order to facilitate high-level comprehension of the content. Existing research has used the categories of extractive and abstractive to describe summaries. In the former, the summary ‘extracts’ sequences from the text, whereas in the latter the summary ‘abstracts’ out the meaning of the text and rewrites it. The degree of abstractiveness of a summary is indicated by how much novel text it contains that is not directly in the original text. Like a summary, an abridgement is shorter than its original text, but it preserves more of its language and can be seen as an alternative version rather than a meta-description. According to how summaries are characterized, abridgements are highly extractive, even if some abstraction is needed to connect the …

Content truncated to 500 words