A b L it: A Resource for Analyzing and Generating Abridged Versions of E nglish Literature (2023)

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 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.

## introduction an abridgement is a shortened form of a text that maintains the linguistic qualities of that text foot_0 . 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 existing inference and generation tasks. we present a novel dataset for this task, focused on abridged versions of english literature books, which we refer 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 relations. the dataset and all associated code, including a python package for easily interfacing with the data, are available at: github.com/roemmele/ablit. ## creating an abridgement dataset the ablit dataset is derived from 10 classic english literature books, listed in a.4. these books are in the public domain and available through project gutenberg 2 . a single author, emma laybourn, wrote abridged versions of these books that are also freely available 3 . the author explains: "this is a collection of famous novels which have been shortened and slightly simplified for the general reader. these are not summaries; each is half to two-thirds of the original length. i've selected works that people often find daunting because of their density or complexity: the aim is to make them easier to read, while keeping the style intact." informed by this, we designed ablit to capture the alignment between passages in a text and its abridged version. we specify that an abridged and original passage are aligned if the content of the abridged passage is fully derived from the original. after obtaining the original and abridged books from their respective sites, we detected chapter 2 gutenberg.org 3 englishliteratureebooks.com headings to split the books into chapters (see a.1 for details). we paired the original and abridged version of each chapter according to these headings. obviously, the two versions already form a broad alignment unit, but our goal was to examine finer levels of alignment. we chose to use sentences as the minimal alignment units, since they are intuitive units of expression in text and can be detected automatically foot_1 . ablit annotates sentence boundaries by indexing their position in the text, which enables all whitespace characters (most importantly, line breaks marking paragraphs) to be preserved. ## producing abridgements the above results show that anticipating what parts of a text should be changed when writing its abridged version is not trivial. the full task of producing an abridgement implicitly involves inferring these preserved/removed labels while additionally predicting the specific text that dictates these labels. we examine models that have been applied to tasks related to abridgement to establish benchmarks for this new task, with the intent that these benchmarks will inspire future work. ## conclusion in this paper, we introduced ablit, a corpus of original and abridged versions of english literature. ablit enables systematic analysis of the abridgement task, which has not yet been studied from an nlp perspective. abridgement is related to other tasks like summarization, but has a stricter requirement to maintain loyalty to the original text. our experiments motivate an opportunity to better balance this goal against that of improving readability. we also envision future resources that generalize this task to other texts beyond english literature.