APA - RST : A Text Simplification Corpus with RST Annotations (2023)

Abstract: We present a corpus of parallel German-language simplified newspaper articles. The articles have been aligned at sentence level and annotated according to the Rhetorical Structure Theory (RST) framework. These RST annotated texts could shed light on structural aspects of text complexity and how simplifications work on a text-level.

## introduction the goal of text simplification is to reduce the complexity of a text whilst retaining the main information, in order to make a text easier to understand. in this paper, we present a corpus of germanlanguage parallel simplified newspaper articles, at three different complexity levels. each text has been annotated according to the rhetorical structure theory (rst, @xcite framework. rst posits that segments (or elementary discourse units, edus) in a text are related to each other and that one component of the pair serves as a 'nucleus' and the other as a 'satellite', or in some cases both components are considered to have 'nucleus' status. an example of a relation that connects edus is 'evidence' where the nucleus is a claim and the satellite provides evidence for the claim. rst therefore provides information about the structure of texts; an area which has thus far not been the focus of much research on text simplification. whilst much of previous work has focused on sentence-level simplification, text-level simplification is a promising area of research, as it represents a 'more real use-case scenario for a simplification model' @xcite . for the german language, leichte sprache is a term often used in the context of simplified texts: leichte sprache texts are written according to clearly defined rules, however text-level aspects, including coherence, are often neglected in the guidelines, even though they are highly relevant when producing accessible texts @xcite . aside from contributing to the general collection of rst annotated texts, this corpus could also be used to help carve out what text-level simplification actually constitutes, how simplified texts are structured and how this differs to their standard counterparts, and could also be used to answer questions surrounding the complexity of different types of text structures. section 2 provides an overview on previous work that has considered questions at the intersection of discourse structure and text simplification, section 3 provides details on the annotation process of the corpus, section 4 outlines some statistical analysis on the corpus and section 5 summarises the paper and provides inspiration for potential use-cases for the corpus. the corpus can be downloaded at https://github.com/fhewett/apa-rst . ## related work datasets which combine discourse structure and text simplification are relatively rare. leiko is a german-language corpus of newspaper articles simplified at various levels (including leichte sprache, @xcite . a subset of 40 articles from the corpus has (manual) penn discourse treebank (pdtb) annotations. @xcite expand their corpus of english-language texts annotated according to the questions under discussion (qud) framework to include six newsela articles and their counterparts at middle and elementary school level. in the context of textlevel simplification, the task of sentence deletion has also been approached using various discourse structures. @xcite also look at newsela texts, and automatically annotate them with a 'news genre-specific functional discourse structure' and with sentence alignments. they train a model to predict when a sentence should be deleted and find that the discourse structure improves the accuracy. @xcite also focus on the task of sentence deletion and analyse various discourse-based features (from rst and pdtb) for this purpose and find that the position of a sentence in an rst tree as well as some specific relations play a key role. the link between discourse structure and other aspects of text-level simplification have also been considered; @xcite proposes a rule-based system -using cue words, for example -to help preserve coherence when restructuring texts during the simplification process. @xcite split complex sentences by using automatically parsed syntax trees. they use a pre-determined list of cue words (such as 'although') to determine the rhetorical relation within sentences to ensure that the split sentences are still coherent. @xcite implement pairwise classification of texts of varying complexity using discourse features, using a subset of 30 articles from the pdtb which have been annotated with a complexity level @xcite and an automatically parsed subset of the simple english wikipedia corpus @xcite . ## corpus creation the data used in the corpus is from the austria press agency (apa), who publish four to six articles every weekday, (manually) simplified to two language levels: b1 and a2 foot_0 , according to the common european framework of reference for languages (cefr). more details on the apa data can be found in @xcite ; the version used to create the apa-rst subset contains articles up to april 2022. apa-rst covers a total of five randomly selected days from a time-frame between 2018 and 2022, with five articles each day. the corpus therefore consists of a total of 75 articles, with 25 at each level (original, b1 and a2), covering different topics such as politics, culture and sport. ## corpus statistics as we can see in ## conclusion and outlook we have introduced a new german-language corpus of 75 parallel texts at three different complexity levels. the texts have been annotated according to the rst framework and have also been aligned at sentence level. we have shown how the relation distribution differs across the complexity levels, as well as how the relations differ in terms of what level they are used in the tree. we have also looked at the sentence alignments together with the rst annotations and shown the specific relations and nuclearity assignments of the content that is selected for a simplification. we pro-figure 3 : average (mean) amount of edus that the relations encompass. only relations that occur more than once in the a2 texts are included. figure 4 : relations and nuclearity of aligned sentences, out of the total amount of relations. any relation ratio above 30% (the line on the graph) is above average. relations and nuclearity assignments occurring less than a total of 5 times are excluded. n stands for nucleus, s for satellite, mn for nucleus in a multi-nuclear relation.