Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure (2022)

Abstract: Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity. In this work, we focus on sentence deletions for text simplification and use a news genre-specific functional discourse structure, which categorizes sentences based on their contents and their function roles in telling a news story, for predicting sentence deletion. We incorporate sentence categories into a neural net model in two ways for predicting sentence deletions, either as additional features or by jointly predicting sentence deletions and sentence categories. Experimental results using human-annotated data show that incorporating the functional structure improves the recall of sentence deletion prediction by 6.5% and 10.7% respectively using the two methods, and improves the overall F1-score by 3.6% and 4.3% respectively.

## related work the previous research on text simplification has focused on word or phrase level simplification @xcite @xcite , or sentencelevel simplification @xcite @xcite @xcite , few research has been conducted for document-level text simplification. sentence deletion, as an interesting phenomenon for document-level text simplification, has been studied in several pilot studies. @xcite conducted a corpus analysis and showed that sentence position and content influence sentence deletion or retention. the recent pilot research for sentence deletion prediction @xcite considers sentence position in a document, document length and topic, as well as exploits rhetorical discourse structures that capture text coherence in general and can be used to derive the salience of a sentence in a discourse. however, while sentence position and the two document characteristics are shown useful for sentence deletion prediction, discourse features based on rhetorical discourse structures are shown to have little impact for this task. compared to general rhetorical discourse structures that do not consider genre specialties, the genre-specific functional structure we examine in this paper can more directly reveal the importance of a sentence within a document. ## the news discourse structure and sentence types news discourse profiling (choubey et al., 2020) categorizes sentences in news articles into eight schematic categories that describe the common discourse roles of sentences in telling a news story, following the news content schemata proposed by van dijk @xcite . these eight sentence categories fall into three groups. main contents: are the most relevant information of news articles, including sentences that introduce the main event as the major subjects of a news article (main event), and sentences that describe consequence events immediately triggered by the main event (consequence). context informing contents: provide information of the actual situation in which main event occurred, including sentences that describe the recent events that act as possible causes or preconditions for the main event (previous events), and sentences that describe ongoing situation and other context informing contents (current context). additional supportive contents: contain the least relevant information, including sentences that describe past events that precede the main events in months and years (historical event), sentences that describe unverifiable situations, fictional or personal account of incidents of an unknown person (anecdotal event), opinionated contents that describe reactions from immediate participants, experts, known personalities as well as journalist or news source (evaluation), and speculations on the possible consequences of the main or contextual events (expectation) ## conclusion we study sentence deletion prediction to achieve document-level text simplification. we have showed that a genre-specific functional discourse structure improves the prediction performance by large margins, when incorporated into a neural net model either as new features or for joint learning. for future work, we will study other useful discourse-level factors for sentence deletion prediction, we will also investigate multi-task learning to benefit both sentence deletion prediction and discourse parsing tasks.